{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Network Traffic Forecasting with _AutoTS_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In telco, accurate forecast of KPIs (e.g. network traffic, utilizations, user experience, etc.) for communication networks ( 2G/3G/4G/5G/wired) can help predict network failures, allocate resource, or save energy. \n",
    "\n",
    "In this notebook, we demostrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demostrate how to use `AutoTS` in project [Chronos](https://github.com/intel-analytics/bigdl/tree/branch-2.0/python/chronos/src/bigdl/chronos) to do time series forecasting in an automated and distributed way.\n",
    "\n",
    "For demonstration, we use the publicly available network traffic data repository maintained by the [WIDE project](http://mawi.wide.ad.jp/mawi/) and in particular, the network traffic traces aggregated every 2 hours (i.e. AverageRate in Mbps/Gbps and Total Bytes) in year 2018 and 2019 at the transit link of WIDE to the upstream ISP ([dataset link](http://mawi.wide.ad.jp/~agurim/dataset/)). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section defines some helper functions to be used in the following procedures. You can refer to it later when they're used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:29.365279Z",
     "start_time": "2020-04-05T09:42:29.360456Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_drop_dates_and_len(df, allow_missing_num=3):\n",
    "    \"\"\"\n",
    "    Find missing values and get records to drop\n",
    "    \"\"\"\n",
    "    missing_num = df.total.isnull().astype(int).groupby(df.total.notnull().astype(int).cumsum()).sum()\n",
    "    drop_missing_num = missing_num[missing_num > allow_missing_num]\n",
    "    drop_datetimes = df.iloc[drop_missing_num.index].index\n",
    "    drop_len = drop_missing_num.values\n",
    "    return drop_datetimes, drop_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:29.567920Z",
     "start_time": "2020-04-05T09:42:29.561650Z"
    }
   },
   "outputs": [],
   "source": [
    "def rm_missing_weeks(start_dts, missing_lens, df):\n",
    "    \"\"\"\n",
    "    Drop weeks that contains more than 3 consecutive missing values.\n",
    "    If consecutive missing values across weeks, we remove all the weeks.\n",
    "    \"\"\" \n",
    "    for start_time, l in zip(start_dts, missing_lens):\n",
    "        start = start_time - pd.Timedelta(days=start_time.dayofweek)\n",
    "        start = start.replace(hour=0, minute=0, second=0)\n",
    "        start_week_end = start + pd.Timedelta(days=6)\n",
    "        start_week_end = start_week_end.replace(hour=22, minute=0, second=0)\n",
    "\n",
    "        end_time = start_time + l*pd.Timedelta(hours=2)\n",
    "        if start_week_end < end_time:\n",
    "            end = end_time + pd.Timedelta(days=6-end_time.dayofweek)\n",
    "            end = end.replace(hour=22, minute=0, second=0)\n",
    "        else:\n",
    "            end = start_week_end\n",
    "        df = df.drop(df[start:end].index)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:29.752013Z",
     "start_time": "2020-04-05T09:42:29.746678Z"
    }
   },
   "outputs": [],
   "source": [
    "# plot the predicted values and actual values (for the test data)\n",
    "def plot_result(test_df, pred_df, dt_col=\"datetime\", value_col=\"AvgRate\", look_back=1):\n",
    "    # target column of dataframe is \"value\"\n",
    "    # past sequence length is 50\n",
    "    pred_value = pred_df[value_col][:-1].values\n",
    "    true_value = test_df[value_col].values[look_back:]\n",
    "    fig, axs = plt.subplots(figsize=(12, 5))\n",
    "\n",
    "    axs.plot(pred_df[dt_col][:-1], pred_value, color='red', label='predicted values')\n",
    "    axs.plot(test_df[dt_col][look_back:], true_value, color='blue', label='actual values')\n",
    "    axs.set_title('the predicted values and actual values (for the test data)')\n",
    "\n",
    "    plt.xlabel(dt_col)\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.ylabel(value_col)\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download raw dataset and load into dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we download the dataset and load it into a pandas dataframe. Steps are as below. \n",
    "\n",
    "* First, run the script `get_data.sh` to download the raw data. It will download the monthly aggregated traffic data in year 2018 and 2019 into `data` folder. The raw data contains aggregated network traffic (average MBPs and total bytes) as well as other metrics. \n",
    "\n",
    "* Second, run `extract_data.sh` to extract relavant traffic KPI's from raw data, i.e. `AvgRate` for average use rate, and `total` for total bytes. The script will extract the KPI's with timestamps into `data/data.csv`.\n",
    "\n",
    "* Finally, use pandas to load `data/data.csv` into a dataframe as shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:30.934219Z",
     "start_time": "2020-04-05T09:42:30.324968Z"
    }
   },
   "outputs": [],
   "source": [
    "import os \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:30.948562Z",
     "start_time": "2020-04-05T09:42:30.935369Z"
    }
   },
   "outputs": [],
   "source": [
    "raw_df = pd.read_csv(\"data/data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are some example records of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:31.060336Z",
     "start_time": "2020-04-05T09:42:30.949794Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>StartTime</th>\n",
       "      <th>EndTime</th>\n",
       "      <th>AvgRate</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018/01/01 00:00:00</td>\n",
       "      <td>2018/01/01 02:00:00</td>\n",
       "      <td>306.23Mbps</td>\n",
       "      <td>275605455598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018/01/01 02:00:00</td>\n",
       "      <td>2018/01/01 04:00:00</td>\n",
       "      <td>285.03Mbps</td>\n",
       "      <td>256527692256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018/01/01 04:00:00</td>\n",
       "      <td>2018/01/01 06:00:00</td>\n",
       "      <td>247.39Mbps</td>\n",
       "      <td>222652190823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018/01/01 06:00:00</td>\n",
       "      <td>2018/01/01 08:00:00</td>\n",
       "      <td>211.55Mbps</td>\n",
       "      <td>190396029658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018/01/01 08:00:00</td>\n",
       "      <td>2018/01/01 10:00:00</td>\n",
       "      <td>234.82Mbps</td>\n",
       "      <td>211340468977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             StartTime              EndTime     AvgRate         total\n",
       "0  2018/01/01 00:00:00  2018/01/01 02:00:00  306.23Mbps  275605455598\n",
       "1  2018/01/01 02:00:00  2018/01/01 04:00:00  285.03Mbps  256527692256\n",
       "2  2018/01/01 04:00:00  2018/01/01 06:00:00  247.39Mbps  222652190823\n",
       "3  2018/01/01 06:00:00  2018/01/01 08:00:00  211.55Mbps  190396029658\n",
       "4  2018/01/01 08:00:00  2018/01/01 10:00:00  234.82Mbps  211340468977"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to do data cleaning and preprocessing on the raw data. Note that this part could vary for different dataset. \n",
    "\n",
    "For the network traffic data we're using, the processing contains 3 parts:\n",
    "1. Convert string datetime to TimeStamp\n",
    "2. Unify the measurement scale for `AvgRate` value - some uses Mbps, some uses Gbps \n",
    "3. Handle missing data (fill or drop)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:31.439100Z",
     "start_time": "2020-04-05T09:42:31.433819Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(pd.to_datetime(raw_df.StartTime))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:31.619691Z",
     "start_time": "2020-04-05T09:42:31.614336Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mbps', 'Gbps'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can find 'AvgRate' is of two scales: 'Mbps' and 'Gbps' \n",
    "raw_df.AvgRate.str[-4:].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:31.817366Z",
     "start_time": "2020-04-05T09:42:31.810257Z"
    }
   },
   "outputs": [],
   "source": [
    "# Unify AvgRate value\n",
    "df['AvgRate'] = raw_df.AvgRate.apply(lambda x: float(x[:-4]) if x.endswith(\"Mbps\") else float(x[:-4]) * 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:31.989842Z",
     "start_time": "2020-04-05T09:42:31.986685Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"total\"] = raw_df[\"total\"]\n",
    "df.set_index(\"StartTime\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:32.169108Z",
     "start_time": "2020-04-05T09:42:32.163918Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no. of n/a values:\n",
      "AvgRate    3\n",
      "total      3\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "full_idx = pd.date_range(start=df.index.min(), end=df.index.max(), freq='2H')\n",
    "df = df.reindex(full_idx)\n",
    "print(\"no. of n/a values:\")\n",
    "print(df.isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we drop weeks with more than 3 consecutive missing values and fill other missing values remained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:32.545450Z",
     "start_time": "2020-04-05T09:42:32.534666Z"
    }
   },
   "outputs": [],
   "source": [
    "drop_dts, drop_len = get_drop_dates_and_len(df)\n",
    "df = rm_missing_weeks(drop_dts, drop_len, df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:32.715750Z",
     "start_time": "2020-04-05T09:42:32.713641Z"
    }
   },
   "outputs": [],
   "source": [
    "df.ffill(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:32.917402Z",
     "start_time": "2020-04-05T09:42:32.913676Z"
    }
   },
   "outputs": [],
   "source": [
    "# AutoTS requires input data frame with a datetime column\n",
    "df.index.name = \"datetime\"\n",
    "df = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:33.110253Z",
     "start_time": "2020-04-05T09:42:33.101643Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>AvgRate</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01 00:00:00</td>\n",
       "      <td>306.23</td>\n",
       "      <td>2.756055e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-01 02:00:00</td>\n",
       "      <td>285.03</td>\n",
       "      <td>2.565277e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-01 04:00:00</td>\n",
       "      <td>247.39</td>\n",
       "      <td>2.226522e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-01 06:00:00</td>\n",
       "      <td>211.55</td>\n",
       "      <td>1.903960e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-01 08:00:00</td>\n",
       "      <td>234.82</td>\n",
       "      <td>2.113405e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime  AvgRate         total\n",
       "0 2018-01-01 00:00:00   306.23  2.756055e+11\n",
       "1 2018-01-01 02:00:00   285.03  2.565277e+11\n",
       "2 2018-01-01 04:00:00   247.39  2.226522e+11\n",
       "3 2018-01-01 06:00:00   211.55  1.903960e+11\n",
       "4 2018-01-01 08:00:00   234.82  2.113405e+11"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:33.292662Z",
     "start_time": "2020-04-05T09:42:33.280539Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AvgRate</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8.760000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>454.050030</td>\n",
       "      <td>4.084920e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>238.377074</td>\n",
       "      <td>2.146655e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>86.490000</td>\n",
       "      <td>8.641890e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>273.715000</td>\n",
       "      <td>2.459111e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>410.590000</td>\n",
       "      <td>3.694360e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>558.200000</td>\n",
       "      <td>5.023054e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1760.000000</td>\n",
       "      <td>1.585238e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           AvgRate         total\n",
       "count  8760.000000  8.760000e+03\n",
       "mean    454.050030  4.084920e+11\n",
       "std     238.377074  2.146655e+11\n",
       "min      86.490000  8.641890e+09\n",
       "25%     273.715000  2.459111e+11\n",
       "50%     410.590000  3.694360e+11\n",
       "75%     558.200000  5.023054e+11\n",
       "max    1760.000000  1.585238e+12"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the data to see how the KPI's look like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:33.826495Z",
     "start_time": "2020-04-05T09:42:33.641397Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ik5/8JAYNGoQjjzxSW++f/vQn3HPPPfjwhz+Mj370o/jABz6AK6+8ssvlAgCuvvpqnH/++TjvvPMwcOBAPP3007jiiivwkY98BGeffXbRKe5ykUwMXsAOIsMAnAvg7QCqANyolHpSRJ4GMEcp5U8ddymAmwG0Aej09h3lbRuMTMaNwwCMBfBzVejkyFiYFyxYYH1hJPkMGjIaALDljgtiloQQQghJL6tXr8YHP/jBuMUITUNDA9761reivb0dl1xyCb7//e/nKdxRo7tnIrJQKTVYt39Blwyl1BWG8u9qykYgk2ZOt/8CAB8qdD5CCCGEENJ7uemmmzBx4kQ0Nzfji1/8Ir72teQnTis16I8QQgghhJDQ3HXXXXGLYA2XxiaEEEIIISQAKsyEEEIIISknRFgY8SjmXlFhJoQQQghJMYceeihqa2upNIdAKYXa2loceuihVsfRh5kQQgghJMWccMIJqKioQHV1ddyipIJDDz0UJ5xwQuEdfVBhJoQQQghJMQcffDBOOumkuMXo1dAlgxBCCCGEkACoMBNCCCGEEBIAFWZCCCGEEEICoMJMCCGEEEJIAFSYCSGEEEIICYAKMyGEEEIIIQFQYSaEEEIIISQAKsyEEEIIIYQEQIWZEEIIIYSQAKgwE0IIIYQQEgAVZkIIIYQQQgKgwkwIIYQQQkgAVJgJIYQQQggJgAozIYQQQgghAVBhJoQQQgghJAAqzIQQQgghhARAhZkQQgghhJAAqDATQgghhBASABVmQgghhBBCAqDCTAghhBBCSABUmAkhhBBCCAmACjMhhBBCCCEBFFSYRWSoiOwWkRW+sptEZIeILPH+fcW37QYR2SAia0XkS77y872yDSIyxP2lEEIIIYQQ4p4wFuanAZyvKb9XKXW6928MAIjIqQAuB3Cad8xDItJPRPoBeBDAlwGcCuAKb19CCCGEEEISTf9COyilponIoJD1XQzgeaVUC4DNIrIBwJnetg1KqU0AICLPe/uuspaYEEIIIYSQMlKKD/O1IrLMc9k42is7HsB23z4VXpmpnBBCCCGEkERTrML8MID/BHA6gJ0A7nYmEQARuVpEFojIgurqapdVE0IIIYQQYkVRCrNSqkop1aGU6gTwOLrdLnYAeLdv1xO8MlO5qf7HlFKDlVKDBw4cWIyIhBBCCCGEOKEohVlE3uX7eQmAbAaN1wBcLiKHiMhJAE4GMA/AfAAni8hJIvIWZAIDXytebEIIIYQQQspDwaA/ERkG4FwAbxeRCgA3AjhXRE4HoABsAfBjAFBKrRSR4cgE87UDuEYp1eHVcy2A8QD6ARiqlFrp/GoIIYQQQghxTJgsGVdoip8M2P82ALdpyscAGGMlHSGEEEIIITHDlf4IIYQQQggJgAozIYQQQgghAVBhJoQQQgghJAAqzIQQQgghhARAhZkQQgghhJAAqDATQgghhBASABVmQgghhBBCAqDCTAghhBBCSABUmAkhhBBCCAmACjMhhBBCCCEBUGEmhBBCCCEkACrMhBBCCCGEBECFmRBCCCGEkACoMBNCCCGEEBIAFWZCCCGEEEICoMJMCCGEEEJIAFSYCSGEEEIICYAKMyGEEEIIIQFQYSaEEEIIISQAKswkFpRScYtACCGEEBIKKsyEEEIIIYQEQIWZxAINzIQQQghJC1SYCSGEEEIICYAKM4kFGpgJIaT8LNm+Fxt218ctBiGpo3/cAhBCCCGkPHztwZkAgC13XBCzJISkC1qYSSwwSwYhhCSHj9w0Hre8vipuMQhJLFSYCSGEkD7O/uZ2PDljc9xiEJJYqDCTWKB9mRBCCCFpoaDCLCJDRWS3iKzwld0pImtEZJmIvCIiR3nlg0TkgIgs8f494jvmYyKyXEQ2iMh9IiLRXBIhhBBCSmXQkNH467g1cYtBSCIIY2F+GsD5OWUTAHxIKfURAOsA3ODbtlEpdbr37ye+8ocB/AjAyd6/3DpJH4IuzIQQEj8rduzD5DW7jdsfnrKxjNIQklwKZslQSk0TkUE5ZW/4fs4B8PWgOkTkXQDeppSa4/3+J4CvARhrKS8hhBBCHHHh/TPiFoGQVODCh/n76Kn4niQii0Vkqoh82is7HkCFb58Kr4z0URS9mAkhJHUMGjIaP/nXwrjFIKTslJSHWUR+D6AdwLNe0U4AJyqlakXkYwBeFZHTiqj3agBXA8CJJ55YioiEEEJIn2drbSOmra/p+r2pugHvHfjWouoat3KXK7EISQ1FW5hF5LsALgTwLeUl1VVKtSilar2/FwLYCOAUADsAnOA7/ASvTItS6jGl1GCl1OCBAwcWKyJJMPRhJoSQ8nHpw7Pxx1e7YvfpikGIJUUpzCJyPoDrAVyklGrylQ8UkX7e3+9FJrhvk1JqJ4D9InKWlx3jKgAjS5aeEEIIIQXZf6Ctx++m1o6YJCEknYRJKzcMwGwA7xeRChH5AYAHAAwAMCEnfdxnACwTkSUAXgLwE6VUnbftZwCeALABGcszA/76MC8vMk4wEEIIcU0JiVxvH7sabR2d7mQhJIWEyZJxhab4ScO+IwCMMGxbAOBDVtKRXsvvXlketwiEENJnOEijMKuQvnGPTt2ED/zHAFzy0W7Pys5Ohfrmdhx5+MGh6mhqbcdBIjj04H6h9ickaXClP0IIIaSXIxoT87/nbgt9fFtHT+X6/jc34L9ufgO765tDHX/qn8bj7NsnhT4fIUmDCjMhluzYewC3vL4KnZ2MXCSEpAOdhXnq2vwFS7bUNOoryOnuHp66AQBQU98aWoY9TW2FdyIkoVBhJsSS655fgidnbMbi7XviFoUQQkJxkORrzLox/yUPzdQen5s7v7kt49N8kKdFVO1vRjv9nEkvhgozIZZ0en5/NDATQlJDSB/mPU1t2FbblLfN5O7cTwR7m1rxib9Mwq2jV7uQlJBEQoW5j/D4tE0Yz2TzTshaauiSQQhJCzoLs6kH21yb75Zh2ldEsM9LWffmmnwXD0J6CyWt9EfSw21jMiP/LXdcELMk6Sf73aG+TAhJCxp92Wg1bmvvzNtm2lfnG01Ib4QWZkIs6bIwc7lCQkiKMfVguvJcH+Ysfsu1aR9CegNUmAmxJBvkQoWZEBIn1fUtuPLxOahrLJypQmcINuVhVkrhiRmbcsqKkZCQ3gMVZkIsyVpU+AEhhMTJ0JmbMWtjLYbNC59POQwKwF/GrMkrM+2bRZfrWceB1g6sq6ovSjZC4oIKMyGWiKcwd1BjJoSkGFMXpi0P0d+Fdcn41QuL8cV7p6GxpT3U/oQkASrMhFjCGBfSV5i1sQb//dBMtDG/bq/E7FaWXx5GFd5edwA79h4ouN/czXUAgJZ2tiuSHqgwk17Jrn3N2FjdELcYhKSaXw9fikXb9mJ3fUvcohANpQ7eTfqyLgPQ0u37DHX03Pnfc7YWPC+NDiSNUGEmvZKzbp+Ez909NW4xCEk1HZ7m1E+Xk4wkBlPwnh/R5mE2Bf3ll41YVIHmtg593UWqwGHkJiQpUGEmkXLDy8vwh1eXxy1GNLCvJylkxvoafOqvbxqVHz9ZS+NB/FIkkqjGMSZFul1jes4teXjKRhxo7W5bszbWYNCQ0T32ySrv7EJJmmA3SCJl2Lzt+PcctxHccUNjG3FFTUMLFm7dU9Zz3jZmNSr2HMCG3YVdlrI+rrpV4khycG2oNbtq5G9Yun1vXtmmmu62dcfYNXnbs61pybb8YwlJKlSYCSEkJi55aCYufXhWWc952MGZbj+chZkuGUnGxhVCn4dZv68pGFBpYvT+d/jSvLK39AunWvzwnwtC7UdIEqDCTHo190xYF1ndXNWKlMr2usIZBVyj82U1kfVhpr6s57N3T8EDb66P/DwNLe0YNGQ0Bg0ZjS01jXnb756wDufcObnr97gVO3HKH8b2cI1wQdhUmv0DFOa731jL9kRSCRVm0qu5b5L7j1m2r5+6ttp53SSYCauqsL+5LW4xegWhVB9vp74em7Vhdz2WVWhcD6obcdcbmUH53qZWvLmmKpLzb67uVpLHrdyl3WdrbVPX338btxat7Z2o3Od2QBZ2ddODAhTi+9/cAObJyMzwjF62M24xiAVUmAkpkmdmb0VLu1sLDjFTsacJP/rnAvzq+SVxi0L6GJ+/ZxouemBm4D5X/2shvv/0AuwJsUx1KbhUNY1ZmC1dNYg9fxmzGtc8twhzN9XGLQoJCRVmEjtxdxjNbR0YNGQ0hs/fbn0svx/lI+tzu6U2f0pax6illRg0ZHSXRXrD7noMGjIaK3bo88l+76l5+MYjs90Im8MbK3dh0JDRqItYmQpi8K0TcfuY1UUdW85mPmXtbgy+dQJ2OraORs1mz1WiNYJFXr76wIyuv7PuDPXNbXhg8obA48oVDFgKlz1SXh/+pLC9LjMj0KBZ7fCXzy/G+X+fVm6RnPC+343BgzntclttEwYNGY2FW+tiksoNVJhJ7LywwF5RdUl9c6bD+tv4/GhuHTY+oMQl4e77tHXV+Ou4Nfj5sMUAMp01APzSs0w/NGUDJq/dnXfc5LXVmLclmg79iRmbAQBrd9UDADo7FZ6fl589pqGlHXeNX4vp66sxc0MN1lfVd21bvG2P1i0gLDUNLXh02ibUNGQWIUnqYO+ZWVtQ09CK1Tv3xy1KIP4sI63tnaj2FneZsb6mpHonr9mN+yetx74DetejbMYSvwtGGLTdVkRtwKZtzd9S3iwxSSGboq9fjv/Krn3NGLmkEmt21XcNwtJCR6dCe6fCnePX9iifviHjvvjSwoo4xHJG/7gFICRushHdzW1cpjUVFPgYXzV0Xl7Zzn0HsLIyo4CNWb4LY5bvwpY7LohCulCMWlaJIS/n5yf/46sr8MriHXigO36rS85LHprV43ex2CpaQHkXmEioHp/H5+/pXhjp4Skbu/7+9YtLcenHTii63u89PR8AsKJyHx799mDjflG6R9gsaAIEB4XqNtHm0B1Q2z8nybk/a855d02JtZ+yxeSimM3mktRBelhoYSZlYen2vYHT4cUwcVVVV+Q46f3oPrKDhozG/720rOCxpQyGTvn9WPzvCxnr9M2jVlm1t5b2jLvPvM09Ldf7m/OnYQGgan+ztvz2scW5UgTBVdaC2VTdoO1f5m2uyyvb01Scq012qnqKZsajpkFfZ3aGS7d89fee8gaLCVdIEy5eWcgqzP/z5Fx85R/Tu8p37LV3Rdqx9wAGDRmNiau6g04rvbIJq6IJRLWhtwyQqDCTsjBpdZX3f/6HoViGO3blaGhpL0sQ35aaRry+rFK7beaGGvz+leXYtU+vOBFgU00j2nx+ooVcesat2FWSNa61oxMvL94BABg6M+NaMWppZajp0nqNYqyUwrNztmr3131Y9je34dGpm7p+r6osn6tCX1apcwc5WUYb3l0/YQ0Di7dn3BFGLNqRt62/IdVEtlTXpifnZe4JfoJMjememoYWrbtVLv47v6pE96Pfv5KZrfK7PCyryLTBF8vo8liom037GJ0KMykL/bxpp47O5Lk9+D8a/qnVqPjivdNw7XOLtdu+9cRcPDt3G654fE7kcqQNv/ow1PMJDsMDkzc4t6b+fNhinHfXlIL75ao8Cgqrd9Zjza56w/75StLvX1nR4/dX7puet0/UpPw7VxSm1Q0PCsqZ5nHh/TMK7uM/R1b59Q8Ec31bs2TF6tSZmAMJb+aLWrHpLRZHHdc8uwhDXl6uzZftx9UtqNx7AFMSnuK0tzxuKsykLGTz2Ldbd/LhyCpEY5bvDJwyb/QWAHhkardifPrNE7r+LpQSasWOfXhzjd5KPmTEslDT9dko+g/fOB4XGJSfzTWNPawFg4aMxu9eyfd57a0MGjIafx61skeZv+XoIsuDKNTsfvNi/mpluRQzo6FTumyt3VGlKfNL8fqyTEaRbOQ+gfEr72qZ8M5O1RWYmu2//P6rptN0W5gLn+Pz90zDJQ8Fp8OLgnNDDCZ7K9mg2rYIsqXo6Ijom1oMhSTxG6fWVWWyFk02fE+TCBVmUha6LMwaZSHXqrbvQBsemrLByoKSrfa1Jebp0oVb6/C8lzruX7P1U+KtHd3nfGrmZlTm+JPNNUzTAuiqW8ek1VW49rlFqNjTrZDUt7R3BaLpGJETUfzc3Mb5CYYAACAASURBVMLTfGln4dY9GLs8k8z/qZlbQh+3va4J/zK4OQDBSmp1fUuo6O1iIrxzlZ5nZm3BwZpV0EYv24nmtg7M2FBahoVi6OhUXTMepqlhk3tCb8akFussv8Xo0P42mZ14y06jB/HglI2BMybNbR3Y5FvoZPG24jOr5OLKhcNmSe+04c+ipJTCE9M3YcWOffjNi0vx6uJ815u+QPaW+Jvtoq0Zd6SxK9KzeAuzZJDI8Cu8B/mmEdsLjLz/NHIFRi6pxGnHHYlzThlo3M+f7/RAWwcOPbifdj+lFEQElz7cnWM3+7HKlSUr8+76Zvx51CoMm7cNb1x3Ttf2fiH6+baOzh5KUXtHJ37wzAIApfuftrR34JD++uvsDfgtbH7aOjoDB1CXPDQTNQ2tuPSM47XbgzyBrnsh3EIoQUpKth3lLgmcqxiMX1mF337p/XnHX/PcIvzvF04JJYcrspczboV+5TigO+r9Z88uSlW0vgtMlrso3Al0A7oWQ6BqdX0L9h9oN8rxpIW7kqlJ25bbkit7Z6cK5eqSNrbUNuHW0d0Buy8trMDXPprpo1zl6/a3nZ4DmvJbnk19pG6AlH3eZTLEOyGUhVlEhorIbhFZ4Ss7RkQmiMh67/+jvXIRkftEZIOILBORM3zHfMfbf72IfMf95ZAk8X8j9NkLPnbrxMDjGrxAqbZ285u0vqq+h9/WaTeOx/e9dEy53DcpP7l/tpMxrd6VnXatzYlUD9Opn/qncT1+n3PnlK6/9xpyq4bl/X8Y18NK3Vf4+G0T8YV7zYn8sxkFTEpOkGXMlO82l6AJj8/8bTI+mPPcARjMlPo2VK4p3FxMga4vL6pAW0f5P7pZ4rZC6tL+AXqXjFJl1bWtBVv3YGN1Q/4GBM+YNLXauSslgV+HcIlKGwrm5zR3U60zy7//+5J0/Hejn2RTzSXHpaQQYV0yngZwfk7ZEACTlFInA5jk/QaALwM42ft3NYCHgYyCDeBGAJ8AcCaAG7NKNumdvOibwvZ/Y8IqKEHopi6nrtMHPry0aDuW5+yf/UCZpqAP9lxIcq0AuYuWZN/13b50YG0dCgu8BTA2VTf0SBPkIneqf7q1r7C3qTSl1kWfrHt22UVRKvc1l6xc5uZjjYPsJTa3deB/h/dUYmZtLL+7SNLo7FTawOBirM6v+tzHJq6u0g5c1uzUB4cq2AdSubCMu1JtckV5pRe5KvivrZ/hps+PaIGkJJH15QagbazZ7k7npplUQvXQSqlpAHKf8MUAnvH+fgbA13zl/1QZ5gA4SkTeBeBLACYopeqUUnsATEC+Ek5IKEzWOJ0lUameS8sCIQKpDJHoDYb8uTfkWKO+7i2x/M3Hema7iEpxIx5FLKwQFp0yftXQuaVX7KFxbY6U7Luiuzc6H/8rH3d3rWll0Tb9qnTFNK/cQFNdjILpXc+4mRVx0tx6bMsd9T19ZbVUU6YTXRxDb8DfOgrlxz8oIJ94Uinlqb1TKZX11t4F4J3e38cD8Ec/VXhlpnJCuqhrbA2lELYVERDop71TdeWG1h+UX9TQ0o6/jluTs1tmR9MouSknm4Pu2uZsqjWIYHIvICYiXf1MU/eBtp5WwX0H2nDV0HndC5BYiJMkJYKDMj1RZfkBgHbNDEWdYWBfyK0t6SSoqUeKyYUvN9bBhk3VDWg1uCv2dA2K9ya3aGT0dytdCnOKNGYnwxyV+ZI4u2oRuVpEFojIgurqZOcXTCtKKayr0k/3RcFfxqwpuE9tQwvOuGWCJvm+BosPuskqYlJUexzr+7sxIJWZaeotsEKPn/57YbhjPdYZcvj2JUx326YTGrPcLjo7TPqmVxZVYNq6ajw0Od9vPnF4l6Pzv+2tFrCoiEoBvPG1ldhndEcyLWwSnaJUVDCgzue7DyjMSnUHu+dSbHxjTUMLPnv3VPxp5Art9nErd/kU0Mz/tRGlpdThbwd+g09vWR69lF6xynO1gPd/NpneDgDv9u13gldmKs9DKfWYUmqwUmrwwIHmLAmkMI0t7fjQjePzyl9bWokv3jutx1Ka5SCoY92T82FwNQIz1RMmxqqptQN7vWVvdcE+toY4neUuW3LPhHWh6r597BrrPMR9BRvL6M+eXYS6xlYsd7hc+02jVvX4rZslSMOHIs6EBUk2btvclsma5a6LYX+zXcyHTeo3kzHBhevFht36gEUg/oDOtJJdOXR2gLFnYs7M6cKte7CysnAft6exNdR+pZD2lSVLUZhfA5DNdPEdACN95Vd52TLOArDPc90YD+CLInK0F+z3Ra+MRMiG3Q1a5Sob7LZud3KslSZ/r1Ix9f065araC1Twv9j3v5mxFprE21zTiEkhk6/rRMlaBO6btL5H+dzNdUZf7ea26JfwTiO2rgR3vbE29L66qqv2t4RaIts10Z0z3R+0JPG9p/RZe1xhM/D65fOLI1VRdYrQ5++Zatw/DYPGJDFrYw3+MXF91zMM6uZ0rhBBg5csFz84ExfcF26FSiM+ufyDoiS5m5VC2LRywwDMBvB+EakQkR8AuAPAF0RkPYDPe78BYAyATQA2AHgcwM8AQClVB+AWAPO9fzd7ZSQGso05Cdac7LuU69YQ+IpZvICmUW27Jjnvm2t25y1WMtKLZtcp9ArAFAtLku5+Bz2C3hQ9Xg5s27OLbjzIF96/iqRLLn9sduGdCnDlE5kgvl7yLYuVJCkEOuvtyIAFnWxwlZ85OXfLPV2LdDgcfF75+FzcO3FdJHVn2eZ4lU+tjAnQN0oh1MIlSqkrDJs+p9lXAbjGUM9QAENDS0ciI67+Peh9iSqrlqkzN7lk7NrfjJOOPaLrd01DC1ZV7sfxRx2m3d/GMq61gAbclLhy8/YVXLwHtkvTunj1mlqimWFIwgA6jcTRnbo4p22WDFfBoKblxfcdaENTazvedaS+r00DhdxNHpm6EYf0L+5jl617e90BzN1Ui0+891jrOkYtrcTPhy3Gr79wCn7+uZOLksOESZHP3pGW9k5U17dg4IBDCh6TRBjZQRLDihxfUt3UUjG4eB0PtOl9hrOrCJYiSzHyUbHRY7ovpo+YC1/KKDMnmIj6jEmymBI3xJGGzohBls/dPRVn3/6mbW3WvDB/mzMfc1vuGFs4AN7Eb17qTkUYKjhew7NzMykj786JmYmSbNsbvXwnPn5bJsNLGv3YqTAXSXNbR8ElnpNMty9UcjSvn/x7UY/f1zy3yLCnHcVcYu4hSplHwiYD857GVjS2FrYElmq1WVW5H09M31RSHX0VnRIxN0T2FD+6dGDlYNi8bfjJv+wyrKSK9H1Py0ocgxpneZgN5T0Wu4iQ/xuxPHIfcyBooJ6Pf/ErP/54lXmbe3qx1lsGhALAW/r3C9wetv9bWbkPO/cdKLxjL4IKc5F84I/j8D3DUsxpoMsXKjn6cmSYO3mLaPKAclNKuesNS4Pn1REgRpjn85X7puPW0atDnSttLNxqCHNwpCzoasldbCap3PDycoxbuctpneXuDobP327MNZwqUqrc23aNrhY06QszGLbf1vPumqIt/61hARAFhQ/f9IalVIUJ2/9dcN+MvNkA/zVvr+t9yjQV5hKYvp5LxfZGbAJb5m+uM/rjaUf/WhfmPjBqKZIHJ+cvQ5xFd39t72Vf+HAnlU3VDbh+xDL8fJibmSQSPX3BwFIq/i7Fpj8yzUYuq9hbqkg9KFePt62uCZuqC2fnSBNUmElZSUeHq/IsJia5f/DMAqOxM2wwYNA9MdWd7YjLnUc7SeisK9bR+jHoy0lW0rP3rxwiZmMUaurTY2FO8rMrBrOB2S4/s4tu/d9z8pdjJwEuewn8luaKtGtfxs2kt7w2obJkkMLcPGoV9h5oxYIte3DvN/8LH3vPMXGLFEhXWrmY5Ugqe5p6fsSVsrcDaxc60dTS0t5pnWkhy88c+XknFdupXtPepg7bReCJqxmCOD4qSYphIG6Itq0byh20o+ELtpdcR5JQyo2BSJP9NPi8gXUpTF1X/tWTc9teWvsdWpgdMXTmZry8aAe21TXh0odnY7W3MEjcGDvPXjLiK4Ug14vP3zOt5Ppt0s39I2fRkp7y6AWds6kWrb5MImnthIKIPhtExCfQkKTnpJTealqO28I+qDSMy8NH2Lxc5WHW0eooK1Jvw2RhLuaWR53XP29m1rBfDImFnECFOSIWbt0TtwgACndkSfh2v7SwIvGdpYK9omPybdZhCm4LCsrZm2cFD3261GB7TfbPyK7+3kZeNpgEzDnFL0Efwrg0tn53W+Vtf3Mblm5364ObZDbXNGLNru7Vc120ZZd53nNnTguxr6nNOmtQGPztKE3fLbpkOEDnlJ/0pYu70sol5PM0Y4PdNFG59ZxiXmqdjEWluDMekzPNZV914rG9JmMe5ghz0MZRjysrtVIqtgQPhS6hj49liiZKy72tIn3PG+XL9ZsEHp1qDlIulrissQu37sGlD88CABx/1GGYOeSzBY8JK2qnUqmcYaKF2QEXPTAzbhGMGBulxLM0dtQKus31WO0bsdzGfJ0WnYqrVbiSRJLcF0zYW8GjkSOtpOnDmSZZc3EzaLSzSLemeK2CYji4X7dKtfeAq2BWuwDMIGyCVrMLnADAjr3FpYgzBRGntQ+kwlwEn7yj8EpESW8QKe73I0f36K58fK61yqzrm+yj0sOP2pPe5sqBfZaMON6E5Dwo8+SF3X1p6+gs+qOaRNIwUHOBbYZ6Wx9ms59177y//tfmysfnGq8zyn5Hd8bs+XLPGtdzSOvjp8JcBGE+DElxdShE0qV08UIn/RoLobsHumtKS5tzQZoHfEn6WGSC/kqv55bXV+GTd7zZOxYhIUbMGc70G2ziOHK5Z8K61Pk/l7NfsptNzeycG7Ph2t0jV6be9k2iwhwRSfoo6ujqxxIuaBziRRkJbkvQ96a3THMFoVQmcCj0/rYLl9gK5ABj6rsYpMm9X8W2oSlrMzEINkv1Jrm9Jlm2OLF1yTAF1YaxsN43aT0ufjC57o46cq8rlm9G0LY8+TICtkXoOtPRqfDL55f0PK+vHaXpXaPCHBEKmTQ57R2deHbuVuyu168THxdxfJyTRvSjX00eZtu8wn3IJaNd02krKFxsESOQhnuQJBmVSlYuaiAZ0/XW7lcR9qdxrFthazQwB9vq70sSnnEUROFp4fJW5RlZvP9/PXxp0XWOXNKdqk43ANcNots6FH72bPrWEKDCHCGn/GEsPvDHcfj9Kytw5m2TEpk5I+ndVpLkS9L0kq4TTXvQ3x1j1+SVKZVJ1RSWaJWI8Cm1npm9Fbv36wfJSWpHQI61x/u/2O8+B+LJxUXWHtsYjGIVyLQq1Fss+qqo0d3CP41cmbfPml378drSSqu6F23rTpt786hVgfsepJlmaGpttzpfUqDCHBHZxtrucxKKOmm4DpOyke3Ikt4vxdFxRnlO22Aa0zbdB+or900vTqiEMHnt7pLrsH52Eep3W2qbtOUumlecr+2ufc2Y0MuXZE+Swma7THWUGAeNRpeM4l6wNC5sMWjIaExeW75V9GwDNvX7KlTU2Qfr/vdDs0KdXAHop2kDurI0QIU5InSd3KbqhrLLkes7lCWpuVcTTYJkz0yl92SrQUFLM67aS6Ttrgg3m6RgcskI+p5987HZ+NE/F6DTp9WY0kcFnjtJL1RI0vmZt8eopBv2N81uFXu/kjRgKYWkX0Y5Bu9zN2sWPknpi0SFOSJ0DfHx6ZtRGXPqpaxbSMLf4y5i8dMz7G9r9XAxiBYxf7y21fUuBVnn72irVBVjwS83LlxnXH1v8n0OC8u2Y0+mD/Pn2M0e5p+qLZXvPjUf+w6EDyJ0SYKaixEXMlq/XxH6U/vrTqOFWYdxIGE1sDSUW9yjBydv0JZf/9IybflDU/T7F8P3n16QX6j8f6bnYVNhLjNxp12yCaAqhua2Dnz27imYvdH9cprF4spaYavouFBqTKdsbuvAraNXOzhDcnDiY2n8oKenUw6Dy6ux+Xgv3FrX5WbW0t6JR6duxIIt3cu6m2a0tOcN8YZUGfzAo2BvUytueHkZmts6EjXASpCXkbVLRrFGg7THY5SDQn2a/96vq2pAa3t+ULXJd/lv49bmlZmUbn+bCPvU0jogosLcx1hblVnnPqpOdcPuBmyqbsTNrwcHAoQlUWnlIqzb9pi2jpT2OJFj+UFP0NxgHG59tu1x/Eqf77ICbh+7Bl9/ZHbefrM21OBAa2Y2q72jEx0p+ELe/cY6DJu3HS8urIhblB64mjWxWSzDtg9MqgvFnsZWLK/YF2rfcSt2Yfj87U7PH+ddcZ0T+c7xGSV6577ws+TmQOlktpdC9I9bgN5KUjuQqHH90Y/yxbKtuTNBH/2UxkwEYrMyoun67ZUIu/1d4Np6ppQqeuWwUt4B07s5aMhoAMDFpx+Hf1z+Ubzv92Pxgf8YgHG/+kyP/cKIXNaFILzrUUql9oNui7sZHD3GtHIFzuPqHbnkoZnYUtuELXdcUHDfn/x7IQDgGx9/t5NzA8mylLsQZWXlPlz15Lye9QacIw0ucjbQwhwRSWkQ/UyZ4yMm6gFDb1QYTVz73OK4RYiPNAT9WZIsWZRWKTVZ3v1jxkLXsb6qO8h5za76vO13v7EujIhlI8xsQzyzAC7yIbjBpAC6CR7rriQ7I1Hq/TZlqikXTu5LgjqM7wydj1oHbqXJuSI7qDBHRFIaRLnTt7ie4k5QX2Hvw+zoVkxc3btTeAXhLOgvQTMVriUp5R3JPbRQXTbvQKH2n9R2rVSy+p04MLmlmBcuscuSUahr7FKYC+xXDP4FkqJ2FbK9LzUNLRHK4qKO/Er8Rdc+l7MYieGcSZqttYEKc0SkpcPtbVOPUUaOR/lMbetO0lSfK3SDLWdTxkXeriisOy7qjHL1r7D4s7TYr2Dpc+1ISFPuzk2fEIHKgOlSl27fq9/f0Dfa6j+FXTIy/xfratTRqYy+y996Ym7X3z/13DCiwrYl3ZizsEgxdZhw8c3oKFDHgq3usuQkEfowR0RvU0R7I65WuYqD3vhNd2GRt80fG4dnj3MLcynHWh7st8hd/GBwxp2g57lzX/myX4QlK24x9zPKibxE+YEafZiNUbVFkVXuir2t901aj39MWq/dNndzd1aXNyJegMf2GbV15GeycFW3C4VZZxkOGmDGYXyKElqYIyKtDaJUCq0gaG+JKlGgYnAke5QkSJRIsXZ36CPPzpmCZtJzDPUbfVhDnOrpmZvR0JJZErfYFeDKhauZDRckqu1ab9BT6Oln/d+LbSard+4v7kDHJMlw5kIS3UzC/uZ2bLRclC1J98UGKswRkeTmoJTC1HXRLOHZpTA7ugNJerGsFy6J0H6ZpPsSJa6UhWJrCXN6+xy0dn6Nhc5Zyj3KtKPwZy7F9fCmUatwy6hMusmwscjl1Kuz0/8J0k8B2GemMOHiXpoGTK5dMq54fA6A4vtQU7D77jLm9QaAzvAG48hRBlls2oXp+T85Y7OVLDbBw0mCCnMf5JXFOyLzNUpSXtss7gLB4gn605GmTqYUXFmYk0SSZDTlajU1XZuFK3R9wd4DmQj78L6p5etPugf79n1DHC4ZRmzyLVtWHeXCQNq6i7yvOoV54dY6nPmXScVVWCRmV5XSc2Lb4iLDiamOdgtXksw5E9QJWlC0wiwi7xeRJb5/+0XkVyJyk4js8JV/xXfMDSKyQUTWisiX3FxCQklwg0ii/6CJWBYuMZUn6JH2xqA/F9j6zBX6bkVxl5Pkw2x9LouTafNqe8f7FZp9B9qw2OGS2sWSVfCT9jF3Hcjasw5LFzljPfryUg0oxR7dP0dhfnNNFdbusnMbcEGS8sJH+c1oNyykFeUiYHFQtMKslFqrlDpdKXU6gI8BaALwirf53uw2pdQYABCRUwFcDuA0AOcDeEhE+pUmfnJJa4Nwhat3M0n3MUpZrNOnJenGOEJndbT9ENunvQr+Ql3z7KLA7UC6n12uKC7TyoVl1/5mXPLQLGsrVZSYL7P8M2hJcr8qxYe9GIpVIPsd1FO1+f7TC/KU6L6G6RnZ3GPTe9Fu6ZOTpD7QBlcuGZ8DsFEptTVgn4sBPK+UalFKbQawAcCZjs6fOBLTIDQvg/8FcS1nHBY710Rp0XFFX7Ewu5syLo5xK3cVeaQZ1xbMkvIwWx5szM8b8g5n99Lmc7WSJFqSJIvJDzYWS7jhlKb+qFSLafE+zLoyfV079h7ADS8vK+o8hUhSN23KfWwjo7P+OEk3xgJXCvPlAIb5fl8rIstEZKiIHO2VHQ/Av1B7hVeWh4hcLSILRGRBdXU0wWmuqDOsepMkq0AuSfQzNhHpi2VZdVwLl+hIbutyjPWUcekfBUeipGI6UkHfTl203aAqdPcg7m9oUvtsV3JFmbYxqltX7MIiOuW4fz/9Dbj+paUYNm+7dlupuDBsuPoGOlkrxFFwZzLftMKUrDCLyFsAXATgRa/oYQD/CeB0ADsB3G1bp1LqMaXUYKXU4IEDB5YqYqTkrWxTgLJ/FHTBOKL/O+LTxlqPC+L+oPtJ6wg9apJ0W0zPyPXsgIKCUgpPztiMPZbL1uaKols4wSUTVlWZAwdjfNubWtvx1MwtADKuQUl6v5I08LIN+iv189La0dm1AMn4lbtQXR9uJTyTNbncROrG58iYEM9y776/y3/6onFhYf4ygEVKqSoAUEpVKaU6lFKdAB5Ht9vFDgDv9h13gleWaqoMaWoS09/qrEe+v527ZLitLuA85X/LzdOOelkiTSuXlPZlYN7mOkx0sCiA9RSgqTyiKeNM3aXXAVj6EuZc6aJte3HL66tw/YjSppbrvTzJTjBc0JS11dp7Vsx9fHXxDpx9+yTM2VSLlZX6ld3CsHibfmW7sCRDPbNnza56q/2duavpnr9h14Vb69Dc1oEf/2sh/se3Sl8Q/Sxepij7aetZyQhlcbEMuO3CUEkcHJeCC4X5CvjcMUTkXb5tlwBY4f39GoDLReQQETkJwMkA5jk4fyIJ0xzaOzqxtbYxcllyye1LNuxuwKAhozF3U62zul1Z0eJx00v+y5x0Cb/x6Gz88J8LSq7H1WpWTqYjXeFYFqWA1vaMo+v+A22GfcwfrtxPtMnXsZAMYdnf3Bb6HSuk99zw8nLs3NeMyx+bgwvumxGqzvfeMBq/fXFpz/Pk/G2WrvwNKUmDY5MopibjJvdz9z3YEvJ7mRv0F0Qc6T9dnLLYZcND1x/lQCI58b1WlKQwi8gRAL4A4GVf8d9EZLmILANwHoDrAEAptRLAcACrAIwDcI1SqqOU8ycB88iq8LF/HbcG59w5BTv2HnAqU09B8otyX4RZG2sAAKOWVZZ8uv5eR2Va4nP4AktfsTgU5hQE/SVJliixz0BhZ9GI8pNTTv+9ogd5msPaO5X1x9L27Le+vjq/jiIuoRidoVNpghf9gdABsiRpAB+HLOZcvpbCWM2m2BtidEF/Lvnl84sxfX2Y+KrS3SZsrbfGuq39jzWyBATx3WdYijxs3WmgfykHK6UaARybU/btgP1vA3BbKedMC2EaxGzPolvb0ILjjzosGkFCBPRkf5bSAe/a14y1VfV4zzGHAwCa2/QKs6k8DuxnEaP7KNgSdZaMmoYW7GlsxcnvHBDpeQphmz/ZeFssb1d9cxsmr40m4DiSR+fVaXtfMspIz4NcTN0Cwc3/taX5g/NiPqKuXrGwA4Qk+Q3b4sJi6Myf2uIAv2IYtm0eFLEP88gllRi5pBJb7rggcL84DRu5p45alnsmrAu9b1oNPlzpLypCNIhy+OGGOoODqZ2LHpiB7wzt9rAJG5xRiDhy3NpOO7rAVQYGV5x35xR84d5p0Z4kBPZWEbtyE/83Yhl+MWyx5VHhiGKwk63Rtk/546sr8sraOzudaKKdSllZwToVsLepFS0Wg2pX09I9AqEBY4NJUCa3WGZNTNK4CJIMU0fYPniXxeJcuW3of4cvCYy/sLlWJ83FUZtz4U/tKqakxz4pUp6pMEdEUtqATo7cDqLLwlzCeXZ7CnJSrtuPq1yQyYqcj1YWp8FfJWDdQdsqOgaFq3Jv/gc36sGbSdktmNvcd57ZlnEIb2gUA9uBYf+DRHutyyr24aQbxuSV6+4tkGnTp988AfO21PnK7GQplrBKZhz5z12txueCX72wRH9Oo69u6eq77T1vam3HyCX5Mxjm4OyevLxoh5P4C8A+HiDSQHHr/XUuGW5aV1rXEaDCHBFWo9Ayt53cVzLbjySxDSdJpihFsTWUJei2OMPFp8K8Ell0dywKq0uP/UMcUGifoM25910phQfe3FD4pEXy13FrtOVxtmm/MiWSLL9h4yljkMXkUhelKP6gvzDYzFAAgK33RpQLfdhg34+Uvw9MUiyAC6gwR0SYfJVx5D/MPa+Cf1Rbnmk1q/qc1hbynMbAFrt6bB6vq2wQAPD6sko0JsRCbIPuilwFtlg/uwgXeYgiD/PYFTud1depgG11TaH374g0I05w3c58mENWFEewUpT9jitsYw1KqfuL907Ftlpz+7T1X44y20SU98UWFy6Frlp/SvVlKsxREWeDaO/o7M5SoRHkoCg7iMhqTgLJuTpTR7yych+ufW6x9YI6UVNd34LVO/dHfh5zlowE4eLDlVPHs3O3FdjffNLcLVc+PsdKFtHWUgSaKgoqi4aurKm1vaAf67XPLUJLuz5Rk6vgUTe4adNRKmlRTrHnDlLWVTVg6MzNzuq3vS02V+pigOVMSY3FrBudz3scUGGOgQ/+cRx+98pyLPNWMMptOvO31OHBycVPiX7qr5PxgT+O8+rOb5i5AS7JdslIjlC2I3Sjz5ym2NW0flNrRgGYvLYaC7fusaw1Oj5/z1R8+R/TrY9zFvRnWrjEsH+UswOurZQ2AU46cu+N7WIWnQqoabBbYVBHY2v+rIitvvzq4sxaWJc9Mhtn3T4p8NjXl+3EvM11efUE5WGOozdKVA5xA8YQeHPAgwAAIABJREFUARd1K8t3JkH3K87PV56rlYM6bQeSpv17tukEPbACUGGOiKAgiANtHXjOZxFasaPnClWXPTIbd45fW/S5d+1v7kq/o5Ojx0p/vt9OMkw4bvtJepXcpXcqnTAK4LKK0lYvM7F0+17rLCj7DAtquMY27VXcy8KGYcaGmsDtn717agnSJAdd7vZC9yp3UJoNSltZmZnN6OhUeHTqRjS3ZQaSuUFY7Z2ZJcVHLAq36Gw8QX9lP6U1TrJkOKo7CreZGetr0OQN6KKMT7Ja7dMybiFZ7ShRwoSGCnNEmJeQzC//gy+104z1wR9HeznyycuSkbUwuxmDOqijPNhKGuXH0npa0NI3bsKqKgwaMho795W+SM7FD87El/9RnpRztm3S1TNy4teYoICXoFMm5Y3VWVJL7ZNeXbwDt49dg3snZnLE5vpbd3Yq3PL6Kgyb123AuGnUqkxqPQ1x+KTGEVNhi20WGhtsg/5cW+S31jbif56ciyEjllsfm6QFOkx9Y5TxGsb9k3NbrKDCnDDuHK+PIM9FKYX65sJWO11nm79wieeFmEQLc4QvVtS5j62m9e2qDlAM9Wd9YX5mhcWsG1BY1uzS+x27mIIPg3mmxrC/sSK788axAmBcQcBJQddXKQXUNbbipBtGY+KqKozLCW4sdM9acpYMz134wrQQRq2hfZutoMFylIJ5Fjw5WodRFgc3xnYQbOt+VYj65oxlef3uhkz9VrIUedIedbh5zrYB0UlKcZcUqDBHRDFtvLNTYalPofErK0u278Xj0zZ1/X58+iZ8+KY3Cvovai3MuS9Cl4W5dFyP7o3J+eOYSk/Qax7G0vXnUavwib9MBAC8pX9mg2nJchPn/93e77hYdI/U1UDCOmm/1dK9ptkkw/4Jm9afY5m7OSpMz2jxtj1QCvjhPxfgJ/9ehKt9OXILPabsEsntHXoXNZPCnHXhCCtjlKQhNVfUhg0rJdWy/kLveinfmiTlG7YeeFjcSVtXONv81EmBCrMldY3RWdZOu3F8j99ba5uwrbYJg4aMxtcenInbxqzu6uDHrtgFANixN5NeZ9bGGq3Fub8mxY6pA3hpYQXaLRWqXNZW2QUMZYm2w3VUuSsfZifTlOGsKFX7M77G/Q/KvOpZxSGJOJEsQVYUE0l7Ak/N3BK3CAAMLhkqP02Yf7GVQu9SP6/dZ10xcpUAU0o8o3IRh0uG5YAsUX75DoRRAM4uEMDpJ2olNSl5mF0Rj/EpnVBhtuSTd7zZs8D4gbbzO2tu68CBHKvG4m17sWpnzyn0bGfgD9Tb09iKKx+fi589m59K7PC39M8ry9WhZ2/stjBlo8b9DJu3DQ9P2agXPAfnywknyN8zjpfcVhaTAnFQ1yxCuroqVwE/1lanKKcj0/UIyoZO0VFQ2jSY09dXAyhsYc62+05DELTJwmwilvfH0TcmynzDUSqpnZ2qyy0iDI0t+tmBUinK8BKpq05w5bn6gL1LoYPBTgpmR2ygwmxJrlJrwrY9NGgWmnhkar6S2qUw+zq/Vs8qvFaTDkrrw+x7Eeqb2/DK4u4IcZ3cN7y83Lg6V9Qk6b1yNa3vKtWSTd3Z9mKIZUoELlwy0uHzXrrbiPV9SdSbpEfXNqevr9GuxPbtJ+cBCD+dnr36dTkzYMaVIQ23K2jBjKhI0gDehHEA76ISSyas2mV5RLCUuYqjzbtkngkMf2dcPecoXTJsSUN/pIMKc4mY/RTt6mkyjIpz68n7bXcaAD0/Mq3tPb9ScY38gs7b3NaB37y4FLvrS8s3WyqR3htLBdj2ybtbyzE6tLJZCmwyGFpbhyKM2DSJ0p5Svz5X6D7od4xdg34lWEZzA5oveWhWj+22Lko3jVpVtCzFkiT3ECMJaroH93Or1gTdz0FDRmN7wKqY8X1P808ci/+9acYvQe3FBirMEWE7NRy2MetcMrKp6MK2Qf+p8hXwTMHeplZj4Es5UQoYt2IXXlpYgVtfX11wf9PKXdq6bWWx3D/K75a1btW1OE35e6oDreVrR6brM92vKGcBTJieQJT+5Wn4QEXhSpBrYc7FaGEu+ozuMevLyZHSNjjbRnLb63zvwLda7R96WXTV8/8sLy6sMB9jJUnweUs9Jkq3tFJm39LQN2XJd3AlTjD77th10LnlWZ+77Et+/UtLsSVgmlBX751vFF4U5fSbJ+C0496G0b/4dMF9XWC+flXwo+dn8bZoFusA3Cmb2g7aUiewzQfbZWmzO40TfvPi0lD7ucmSYdpimhqNDtOH3qSkHXZwvwilST7bDJa6/v2CnlJpT/ClAGUnKbiaxYySJM2OHHvEW7TlxY67bh0dPKtw36T1+MRJx2i3RWmgCKpatylJ7SVJgz0baGGOiEmrd2vLzZ1fuAa0a18zmts6uhQgv7KsVLgV1YJWafOLkV0tyyXHH3WY9TFZC9OUtbsL3qdYkvM7qdyq2Nr3UiLwyfi7txgEAPxrzlY8P2+b9vnM1qQtq2lowbNzt/Yo04lmP43oZgrQSTJ/y3MeefjBpZ80xVzzXH7QMqDP9JMlvHVQ/zDmb9EvHx9HgJSJNCxcsv+APijPdF+iXHreFZuqGzB1XTVmbiicdnGrwWhlO46wXZbehD6nefl9kntb0B8tzBYMGjI69L679uv9bW3zFebyhXun4ez3HmvocRR+8PT8HiXWK8hZ7u+KoI9C9ntZ39yO0ct34sKPHGesx25p0WiDIExTyZmPSGl32vboKLJkDJ2xGb/6/ClQSuGP3mqVnQq48hMn9thPdxe+//R8LKvYh8994J34jyMPtT63cao3BR2xUcYUyB4H2ZSIOgq97tl30HrYlaCGlBxJzNgHlCWfr94/A40+d7Js36m71EP6m9qo3ZVurmm02l97RqXv5ZN0z5OUn9oGWpjLTunWAp3FLsuS7T1dEgpVm+t/laQPBZCR32+l2L3fbB0H9ErqfouUREHEkWHCuKKdpWtP9h66nDntUkZ8deZmIfDv56dyb2ZAuXib3sKXxbY5GoP+7KrRWsZc6blp/VjEha2130VwsO17EukUs6M2HSV3ji/s5udHpxhGPY60tXY3WsRevMWgMLt41YtpW1of5iibqGUbTVLbtYEKc0KwnmI31OP3JRs0ZLRV/kogfEO+8P7pGDRkNH4b0j+1lPP6Z2SD/Rnzc0ynnST4Lw4aMhrD5m0zbveLogsU1Sk82bKf+nKF6n2Y7S504Va9Am5M72Ty+daU1zQED9ZySdJHIc06ukn2kUt2YLfGveznz3Xngu96jElSgC0xT3cnR8YkEdVt6Qr60zyPQw3xB0YDhguXL2PdopUxSQP1tLZdKswhiXo9d3O0dvgPfTnb4IodGf/moAhhHdZuEEr1uNZ+5dCIo7boOLiEYnNCt3V0oqnV3uL+vKcwX/n4nLxt/meqU5j9jyw3jSEAjFhYgRU79jkJVBk6c7O23IUP896mwvEBYXCS9SCd35uiMN0X02JKc32LLxV7m9zl3C4dW+tdHKtXmohnFbloHkZQrUccYlCYY3hPlVLa89oa5VzJUu5zRgkV5hDUNbY6m9Y3tRPbFadMnWKp+mRtQ3RLfwNA5T473+4M3ReVDQAyuipYyBL1VLpdKl/bgYSp3DTAykjzp5Erceqfxmv3aWxpx+BbJ2LWhhqNfBlm+VaFbPQW2/GfsU3Tjv0rtZ1xy4SMPL7tv35xKS68f4ZWJlfE0T+b3WbKL02SLKa2PDmj+EFQ9hkoKIxaWhn6nNYKs93udnVbvuvGZ50cPTpR2Cr1uttu+h4nyaobg9eQef8E3RYbqDCH4IxbJuDcOydrt9k66dtGjdqkD1Mo3QL7mxeXGvMv1ze7sbDZoBRyLMzBTTYOS4+RKFf6syi/7oUloT4Ka6vqUdPQok07qOv4tamktB+TbrIrWmrdNDQyuXqehQYS+bLY5CB1NdixqqbPMHKJXtE97fi3FTz2l88vAZC5tz8ftjhv+4cMdVgHsTl4drY5i23Puam69IAyW0xv0VsPyc834MoaaZty0wW26y5Y1V1EJS7yMEeJMvyddKgwh2SPs+lYffOwtTCbKCXJf5YNuxu05TUG67Numt0l/isqtIjToQeX3qTT4DNoM732yuIdodS/7PXZ5rLukYRec+90bdImqK63kaBm1GcYu0K/XPIZJx6tLY9jGWFXufuT5JJh4gP/MSD0vlHPjoS9X92zFbptwceUGxsfZhcuYvbB2ensBKkwlxlTM3Gx4pRSqqRlZAvJYsqH+tdxa0o+Z9CV+qf0s53bIQbF+KsBKedKJVGvuLVLRs/fyyryleKKPQfMpwu4eH/Hqttv5z5zvYWI64MTZVCObbkLUvp9io0k3S7zcu/llaMYbIJqXRGP+5Xd/lEOaUxGs1JT2rokDW1XBxXmMmMd9GcZ3R9lTNxBhsp1qcRsCZqmtulcrVxSHLnH2OLiY2HvT93zpBc9MDNvn6BVzwIV5pxt172wpOvvRdv2aD/4YV0yTNjOpMTSQcdkdTrphtGRz/okgUIpJkshjoGauUknxxfeFVZWcM1lPj1ri/UzqthjXhHXBu2iIIZnEYcl1T7oz6BjRLwOahqhwpwQOiy/b7rGvKepzSp3ZBiuf6k7bZzJeh2p3zB6Zsl4zQvcMZ1z1U73qxP6ZdFh6lYiXf3LsjyMfnlwgL9L2EesVMYFJMuanfmDqdb2TuzUBH9qpzpDnrcQUX647DPfRItS+St+pvPzFMwMTXCqLcZnZzvecHCDXcW3JAnjwNZB15i75kAWkwL4lzGlzYQG3e4on1ExPuw2fWkcM1sJWkndCirMZcZ2JHrsEYdoy6Oc0vIresMXdFsdzUEpDvz3Qsrz5hr9kuNZpq8v/SM6ea3+HK5echeKtKu83X7OOWWgcZvpI7Sqcn+Pc45b2dNX9HevLM875vVl0WUriLoeJ+c0lC81KAD6KoIvKI50Xr2JJK1cZ6pbF8SYFub5Uv8VS4thFiWyZ6HM9ZvaS1yKYbmXxjbKYYwH8v+dHu25ZIVZRLaIyHIRWSIiC7yyY0Rkgois9/4/2isXEblPRDaIyDIROaPU80dNrqWmZIwWDf2GjgQ1JpMomx1EX4cdoWf9qN2M3PWV/HuOfpGOKF/s+VuCV7zLpdi0ckEMHKAfnAWxeLud3IA5wNVm4ZLn55sXUtFha3m3cfmoa9QHw5r1Zf0W22BLEh3WK/3FMIPR27C1gLqaNYo0XiFKE7PlIdHOVNgOMNPZqF1ZmM9TSp2ulBrs/R4CYJJS6mQAk7zfAPBlACd7/64G8LCj80eGKcVasejy1ALmDvrFBdu15XGkCDI1clNuZRd1K6CHJvWzc/+z5HMVS5I+XDMN09E2H/qq/T2fW9B3I2hqLY7Ob3td8YGEYbD5hr7sc0HxY5smy+bDXWiWIvccabLilJN/zdlq2GKpADi4vdo0jUivcuEK2/coqqaerVabss02k0mUM8QiBh/m6M5pIs3uRDqicsm4GMAz3t/PAPiar/yfKsMcAEeJyLsiksEJrhv2d4bO05abLMmvL9upLd+xNzqFIcw0ivNzBtTd4xF4D8SFKLbLhpuYsKpKW25qO222DusaTGmyTM/uII0wNoFhQQEj1nlSLcpNdX/7rPfYndM2SDKGLBl2daf0i5MS4lAu7n9zvbbc9v26d+I6B9IkH6UyfdjNo1Zhb1PxC245edfjcBAOSn9qodS7EJJp5cKjALwhIgtF5Gqv7J1KqaymtwvAO72/jwfgN5lWeGU9EJGrRWSBiCyorq52IGLxuEjTFoYO6yiT6CiUfiYKa1WQQuafHm93oGxmsfV3NikpuX67WUwtx2RJcoKF9bIpJ0A0qKmv392AyRr/8c7OaFU3W7cGYz2G3U05x6esLb3fidK60taRHB/b3oirQaANue+jy7p7Ix1KYfTySgyduRl/GbPatyWaO9b13bOw3sY1sNWd1+w2EqEcvazxulCYP6WUOgMZd4trROQz/o0q08qsbptS6jGl1GCl1OCBA82BSOVgY5lcHxKkL5sDGAy9gk0SelsUeiqerhZ4KQbbZ3TAsTtPGEy3R6cLf+nv03r8XlaxL7DuSWvyLekdqogBlIWMpqqHzdO7KplPqa/o5UV6dwo3mNyvom/DuYMflaD+JQ3EsdKfue70ah2RWm/RnV2qFCPEmOV6g4eJFxfm9z1RBv0VM+Nr4zbiRkbL/X0HpKl1569PaYlSaof3/24ReQXAmQCqRORdSqmdnstF1jS1A8C7fYef4JUllm88Orss50lScN/2gAUsgPyX0cXqgoEuGb7qsx2j7UfkHQMOwe760vK22n5ETe40UWJMfRfwiLbUNOL6l5Zh3pbgyPX+mmXJlbK3odgsI+tqZcxEZckwUF1i+wzi5tdXRVZ3byQ5vXG62VITncHJ1KVF/a7fOnp1XlnSfHVt3NvisIKn1aWsJAuziBwhIgOyfwP4IoAVAF4D8B1vt+8AGOn9/RqAq7xsGWcB2Odz3ejTJMmK8AtDuiLTjJQLp5WgUbQ/wKkcDjLfOVvvH/vbl5aV4ezREDSo+dv4NQWVZUDvB33/mxtKkqsQtYYMFGnAdG/+8OoKbXmUMxIjFpkXpSH52PbHUSoACfo0WONPS1qIQq6AcRMkhznoLyaXDO3iKqZ9ozlfEEmaUbehVAvzOwG84n2M+wN4Tik1TkTmAxguIj8AsBXAN7z9xwD4CoANAJoAfK/E8/caHLrmRoYxoMyBY4/5fVNW/lgmbIzgAw492LL25FBKHuZC6NY12XegzeiqYyJKGY3njLBuE7aL6DB1cnJIUlBSWq1x5cBGMSz9XAHbijgmKgwxfwFuIxEO9kKUT1lbjar9zXjn2w6NTA5XlKQwK6U2AfgvTXktgM9pyhWAa0o5Z2/lxtdWxi1CQboszDkvmIuFOML6YxX7aldZLKObpI/lk9M3We2/xxQtHvCIwj4/nYUZsA9Ai4MkPdNyUtPQgiemb45bjNRh7ZYfYfNKqzVOx7nvH2gMqN1Wp1+6Osy99fdhcbzqUS47bbqe8YZgc9MxSVpdL/e+/P6V5fj1F9+PD77rbTFJFA6u9JcQahqi8190RbaJt+aYw5fvCA4Ws6k7r9wwWo7S6JKkjmVLrf4jYuKXzy/Rlne4UGojTJMHRLyseYKeaTnIPqo/vLICj0zdGKssaSRJfUCCRImFoTOTMeDbVteElnZDJpMY3En2NOkXVVNQ2KVZG8E2n7ULwuannrh6N778j+nRCeIIKswG2js68edRybf6lpNOpbCnsRWn/mm887qDfKDK7d+9aKv96nVJ54kZAR+dkMYOk1XEVmGOQwFIg9Lx9iJWWjSRvV5Xg5m+hrUPs+X+09aFT1uYpPiWUinHpYwz5Kh3wT8m6nNl22aWsqGYDBRffWBG6P1jcclIaZumwmxg5sZaPDVzS9xiJAqlgAcnRxPkFeTrpNt2/YjoAvDCBMCRbmyVsjhTAyaZrZazCUFkv0dlSiPf64j6e36VYQErHb3pbYnqWhZt6zZyRGmRtp0JNqWsG7k0fHIwV8ql2W2k9LoDQpAiO2ccUGE2kNYRULQobKmNJk3Q9YYMFPZZvElUDJu3TVv++XumactNxKIw99H32UXKx76IdR7miOSIvvLy4vK7un1PU9et2VzTiDHLd2JPxFl1TLNspvZiMr7M3FAb+pzFWJityi3rt8EmhWgaoMJsgB+afJSKeJU603lT+3r1LvYd0PvM2RKHm0Bfa0HZd4a9WHG4UlI21zRi+AK7RXbyZelrrTccv3+lZ3rGnz27CB+9ZUIssqRhtTxTPS5cMpIUJBslVJgN8EOTj4KbJYNt+NLfp2H/gfau31tqGrHCQZAh6abcbV2X+D9q0tpBF413vabMJiQYWyXClOHhogdmGGfPwtKb2m5vuhY//zt8adwidGG6xcY2HYOyn9asRVSYDfA7k8/S7XtjOe9m32pRk9bsxoX3hw9oIARIbwddLNmrZT9WHLbNZaohiK++OTPYP/O2iUXL0ptc/l1by5ta2gvv5JA0vE+22TAiXXTHsjzpUGE24CK3cG/D1ZS8LWl9uUhy6GP6ctf10sJcHK5jWHbXt2Dxtj1FLX/em1wyXL+HLe19IwuMi9fY1I5cDMiC0sLabUg2VJgN8DuTT2xWupS+XCQ59MUW1NzWgdHLd8YtRiqJor1c8tAsXHCffa7Zu8avjUCaeHDdlfM7nY+tjhrld92YmSOyM0YLFWYDjWWe6kkDcU0NpvXlSgsMcO19KCjsj2lGqDfgIn+ujt1FWJhNC1Skkd5kLS8nVjqtUTF2ULcBU1yRaaYmqvcrakpaGru3srJyH67+18K4xUgclXsPxC0CIUXR19JEzt5Yi2Pf6m4hlL5GSr/nicf1a/iXMWvcVliANHQjZqtudNbeOkM6v97mw0yFWcPsjeFzJPYl/jl7ayznPdCqX46UlM6m6gaMWloZtxiRk4YPnUuSFLWfRvpakGi5SPtdTUO7MA32zBbm6K7JlHM/rQNSumSQxBO4rDMpie8+NT9uEcoCp4KJDSnQi9JJyu/ruJXRLbvtirvf0Pu8P2xYpTfKtm5SmNM640eFWQN9OklfobWPRJmntH8mMcEBVjSkwUIbRDZNYJKpNbhHVO5r1pZHqbym/XnnQoVZA9Vl0legYkBIPmmdMk46CsCAQ+gJmiSibOvT19doy9OqR1Nh1mCaRiCkt9He0Tfaet+4SuKK3mYZSwppnYrvzZieyIhFFSXXbVKY0/p+UWHW0NrR+6apb//vD8ctAkkgpum73kZaO2gSD30x8Pu4Iw/t8fvwt/Rzfg4FDl6TRhyDmLS2ASrMGnrj6kF0MyF9GerLxAaTZcwFm6obIqu7WA47uB/e0r+nOrDq5vPR7yC3Xw6lOHhNGnE8jrQ2ASrMOtL6NAPofVdECCHp47N3T41bhDwm/vocbblrQ4sC0MQ0oYkijjiWv44rb/5sV1Bh1nDqcW+LWwRCCCGkLBx/1GHacueqVC80RqUdhmyFhwqzBr7ThPQuGGxEiJ6BAzIrQp7+7qMiPxffwuTBrjE8VJg1sP0Q0ruoa2yLWwRCEsmcGz4HALjj0o/g9Z9/yurYcb/6tNX+9F9OHuNTsBhLUqDCrOHPo1bGLYJzGPRH+jJDZ3K1SEJ0ZAP7Dj24Hz50/JGRnov6cvJYsn1v3CJ0MXNDdMG2LqDCrKFqf0vcIhBCCCGJo5SFcMMqzO9/54DiT0JSy7eemBu3CIFQYSaEEEJIHu/w/Jv9SI+/7bTnsAZmum70Xar2N+Om11aiPYHrYVBhJoQQQkLQ33Fe4qTz4k/ODtxua20OG3xLdbnvMmTEMjw9awtmJnDxICrMfQR2QISQ3sgdZVzF9KBS/BFSyAlHH45vDn53jzIRwfhffQYPfesMvPWQ/pGcVymF/znrxJLrGXWtXRAjiZ/Ja6v/f3tnHh9Fle3x3+lOZ9/3fSM72RMSliSEJISQoCyCBtwBUVRWEUFFUUQYnVFx3J464yw6OorMc57jzqiPcVwGERdQRxTGHbdR5zmOCt73R1V1qqqru6s7vQXO9/PhQ2rt6upbt84993fOARCamY3YYA5RVveVB/sS7Gx142VgGIYJFjW5/g1U03B02csAgJ/MrtUsE4DyzDj012QhOzEKW88ah8KUaFPnMmsDCQBXzBj+QCg93lFSwowMQs9cHobBTER5RPQkEe0loj1EtExev56IPiCi3fK/ftUxa4loHxG9SURTfPEFQpFl3aW46cTGYZ0jMSrcR1cjcRT28wzD+JD20tRgX4IhgfL6rptWhSNFkdGQ733OZf3tbi5MRkSY1dSxpqvK+chaOsxVOUYuIfjTDcfDfAjAeUKIKgBjAZxDRFXytmuFEPXyv4cAQN42CGA0gD4ANxGRuadshDG7Kderl0vf6Eys6i3DnKbcYUUie0N0uPOfIgTbLcMwAUCdrSA9LtKjY1NjA+PdK8uIw9yWPL/rixe0FR0xkoyeygyfns+sIWzWfvVV0F9WQiQSomw+ORcTWIJRstsdXhvMQoiPhBC75L//BeB1ADkuDpkO4B4hxHdCiP0A9gFo8fbzQ5XTxhciLzka5EXHSgSc21WKq+fU2XNj+gp3HdXm42qdbgtBKRHDMAHgkeXtuEqektd3aZOrXBtdf7uoG1sG6/11aXasFsKmWbUoTovx+2eNdHP5V6ePwVOrOrF44iivz2GUGWN5T5mpYwMd9EdEWDUldOSNzMjGJxpmIioE0ABASaJ3LhG9QkS/JKIkeV0OgPdUh70PJwY2ES0iop1EtPPTTz/1xSX6hZWTHTsJZQrIm45V/UKyWYcWvJ0KndsyFDSRHOMo8VB/nquOLBTF9wzD+B8iQnW2pBHuLE/TbHPXLXjjNFC4+4yxXh9rltlNuab3/euaLgAjP+hvTGEyClNjYLEQLuyvwNjiZId9fjqnDtccX+f8JAa3oL8my9Tnm32T+PKVw++vkUko/mzDNpiJKBbA/QCWCyG+BnAzgFEA6gF8BOBnnp5TCHGrEKJZCNGclpbm/gA/U5ASjQObB+zLt5wk6ZNrchJw8UClZt9DssFspmNdN63K6bauigxU58TjiZUT8dsFrXj6/E6Pr9tqAe5c0IqrZ9diyugM3LmgVaPBu/nEJlPnCcF2yzBMgKjKjsfey6dgWm22bov7nsHbl155pv8LVyzqKDa1X01OArITo6SFkW0va5wkizpG4Z5FjgHds5tyMavR/GDCIwIsyQCAH1nHPCI54gxmIrJBMpbvEkJsAwAhxEEhxGEhxI8AbsOQ7OIDAOr8NLnyupBn/oQizXJfdRZ2rJ6ESRXpWNhejAObB+ypjQ7/KCXbDg9zf2trddHd6qmuhCgbHlzSjpL0WABAfnI08pPNRSIrWInQVpqKOc15IPnvO06Xfo7r5zZo9nXVOEOx4ZphXHFKsC+BYY4IosOQkBSWAAAeJUlEQVQd04dVZMa7PW5SRbpXn+eNXeqvfuq/Th5yLISah/mCvgqP9h/u9eclR+Hq2c7le77Cl78l28uMrxhOlgwC8AsArwshrlGtV8/NzATwmvz3HwEMElEEERUBKAXwgrefH0hOHV/osC5PZ7wO1GahrSQVS7tLAUi6uqtc6IIBzzoFIsKTqzqxrLsUWQna4Bt9AIeSv9Jqcfx5J5al4cDmARxbl63xNlgs0vl3rZuMV9f3ajTUoSi+N0MgNI0Mc7SyvKcU284eb7hNMeQSomwYqHU9Xb/9vIkO6wJhl5rtf9WZFkItS4an92m493XH6i5Mr3cVqhQ6nDy2AABQl+d9RhAmeISi1TEcD/MEACcD6NKlkLuKiF4lolcATAKwAgCEEHsA3AtgL4BHAJwjhDg8vMsPHeIibbhzYStykzzzAmtw05lZLYQVk8vw4JI2/G5hq329osW7cV4jfjO/BXnyNVjd/LopKl3z1OpMFKXGIDkmHHGRNu2LIRRbrglCzBnEMEcUYVYLGvOTNHI1haLUocGqoiFd1auN+WgpSsaVM2uQo8gddOeOjxzyapvRG7vqplxlAXLHd4eGSvQOR5ftDzxNmzYcD7P6NzXi4oFKNBckudwHACJt7s0OX0gyjqmTJERNBUl4bEXHsM/HBJZQ1J57XaZHCPEXGJt4D7k4ZiOAjd5+ZqC59oS64RnAfiIlNgLjS4ZSNvVVZ2peWnHyi2Zimevp0ObCZNxx2hhMKEmFTWddx0fa8Pk33wMYsfayYTQ3wzD+R22XySo1FKfFavZJj4vAvNZ8fHfI0W8SGWbBT+fUYdFvXwQAU3I0Zy/Y5T2lOGviKFSse8Rh2zXH12HlvS+7PG98lH+q2TljWXcptmx/y9S+nupzve0R39jQ5zZz08L2YixsL0bhmj+53O+JlRPR9pMnXe7jC1tJ3R7KMvyviWd8yzuffRPsS3CAK/0ZsO3s8VjRU4aZDbkYU+gYRWwauX+Z3ZSL+xe7r5bnTWeWHueY67QhPwlvbZyKNhPZNSZVpBvqre87axwuHqjE21f2GxwVGFIMMnt4Qog5gxjGNO5StgULJduOOouPml6D61YkXRaSgpAVOkqlgG79wLYuNwFhVotGe1qfl+g6cwOAxGjj/mJ+W5FhXyAgDLXZatYfU6XJP+3LYDRnrDDIvuQrvPUwR9qsDk4VbzHjhPLFfR6pjh4zzGsdftlwfzGzwTeSnc0Pv+GT8/gSNpgNaMxPwrKeUof1AzVZqMnxrgxrU4Gj4R1mJbxwUbdX5wOAp1Z1Op1qGm7nVpwWi4XtxbBayKvRvlHKPTUtRe4HIred2uz5B6sIZICOol33lmPr9BkImOGgBMuOVOa25LnfKQg8c0EXHlrajufWGvdbG2ZUY25LHrpUwX6njCsEIA3k1YP4Oc2SzEL/mC5sV7JXSB1PY34iOsrS3GZuuNmgump3RTriI21O+wJ3XUSyrvhKKM0SNxckYbDFM8NppDgRfHGb3f1W+qD7kcRpBnFVocIIaWJewQazB9x4YiP+Z0mbz85Xn5uIJJVXxFN9XGFqjFOvii/xNOhvYlkalnaX4sZ5ji+wv13Ug98tbMVtp7g3hs1ON+ozfgQDI0+/J5zbVWL/exQHK3qFOmPAXA8NiVDiqVWdaC81Tqc5x4Pcwc44f0o5bj+lGfcs8jzXcVS4FVXZ8UjRGZIPL2vHM2u6kBEfiU2zajUD9gklqTiweQAZ8dpgZaW/U/d602qz7N515fFXHzc4xvlAIj3esRKh0qUa9axmZB76PiiUdJVbF49Hmof9TqhpsJ3hz+DKB5e04bm13XbpooIvni1f4koCU5IWug4BXz0hoRZgC7DBHBCM+thZDTmwWCjk0hQZ4cpuNSqIotA7OgPhOk93dLgV40tSTZUrNSvPG6jJwvKeUsToAnuc3dpptVl484o+cyc3yXArM6rbSEpMYEoKH2ks7hyFG+c14pJpVSHtgXFHTlIUbFaLYaCbpx5FI6pzEtBTlWFvs00mArXcUZkVbxi8ZwZ1H3jDvEZE2qTnuLsyHXOacrH+2NH27R1lQwOJM9qL8IxcUERBCUwbUyh9JyV/st5QvGp2rVs5BuAoDWjId3+v+kZnut3naODV9b14alWnV8cqbeKFC7vxq9PHeHkO4/XVOQnITIi0a+sV6vNDK5uGs8HZcY25sISiNeljFnd6X43SX7DB7EdcNem0eMkosloIZ8llSkP1EXDlVWk1kFYoe9usFvx941S0lUjTsCt6yhATYS6A5q6FrSg1Ma3+8LJ2WC2E5T1liNV5DPRR3YoMhIgQEeZ91LwRZvuvs0yUpBUQASkprCfTwEM30hiozcL8tiKfl5YPJEaD6C2D9Xh4WTuaCoyzUnhCnTwVrTzWBOm+pcZG2GdritNi8HsvPNBmWNhWhDtURpDydfWzNBFhVlw9p07jYW4vTUVdbgIeW9GBiwaqHIz0h5e1Y89lU3DfWeNxw7wGXNgvFZbS39FpblLdKei7vpsMZB9qJE/60T3gvXp2LR5c0oa4SBsK3WTWcIbyDKTHR7rNzuEMd950ZzppV7MYgcTZWzfU07z6ahYmzCAtbrAJvSs6AlEa+MuX9trXqTW+FQGoajUcXDX/jTNrHNYlRWu9x8r3bywwP4KvyopHUkw4Th1X4HI/V53pSa0FuEuVfm+e7J0z6kYnlXtWUXLKaG1wk9mZAjMplYRAUHKdPr26E0+t6nQa0KVQmOK7zDEtBkG1y4apBzdDMJ45JT+6noeWtmu8yYqt36DyePVUZqAyy7hQiFkD7cDmARzYPOAg4yKSUlLuvLgH/dWZOKE5D786rQWtfir8c/G0KkwqH9I4ExFuOakRfzzXvdwtLtKGB85tc5r1INJmtQ/Kp9Vm273V6sezqyLdlHcZcOz7zAz4R4rswV/Mac5DtZexPgrz24aKhSlBoamxEaarMwKOM4znTBqFMHV9Ad2Pq8xoBsN7GxFmwf5N2gB7IZzExvjZXi4YZv/uq8sLxWEBG8x+JELurJUcoMr/VovWw9lVmY6G/ES/Rkf7g50X9xhKMjbMqNYsD3myzHdEZh+WCCcVFZ9d2wWLhTChJBVXzqzB/AlFaC9NRWpsOM6cKHW66rywnj6cN+j02a48moUp0XbNdrNB8Kf0+QJXyPdN0X/mJXs3xe0tEWFWFKbGYPclvZr1WQmRuERVxv2+s4yLVXjDbxa02P/uKEvDmR3FPstEcPLYAs0UvhozeX19zRUzajRV42Y25GDzrBpUZcdrBimKwTVPJb/Qv/wvU8kUilO90zMaeYLCrBb8ZHYt8n04KDJDX3UWMhP8N8OhNmKXqOIF/GEa+dte3jyrBg8va/fvhwSYpd2lmpmTBSqDWSEq3IIL+yux/byJuPdMz7NOnT+lAvtUWZ/0nlrlebCQcbYXf0JkPNAyCp73tyGZGju8GZLDPwpcd8LwZ0j1jrdQgA1mPzJQk4WVk8uwZmqlZn12ovbFEB9pwx/OnuD11JPfcfKEKg+WOmXeTSc2Ij5S29A3zqxBf00mxhRp9X/9NY5av9HZ8biwv8JuiLvqHLITIjWdzPWDDRhXnIK9l09BVsKQsTmvNR+XHFOFlNgI7Lx4MkZnS96PMzzwVihYLYTzp5RrPBWA5GGucuIBfOr8SZhclYGXL+3VZAn49fwWFKt+89wk6Zpt8iBgx2qtPtMbTh5b4KAjd4fei3bDvAbMbyvCq+t78caGPo8DjQBJUnD/4vEOU+iRNqvdW3/WxGKs7a/UFGPQa1SN+NPSNryxwVGTvmFGNTbqBm8KozzIomFUic4ITwM/ZzXm2DXJej0lIL1AlUGUXkJkVH30VpUxbgblLh9t+crVOmSl/+h2UsLbm+llo/t5UX+lwZ6STMxTic1gS75mtuH1y/twQV8FxulmBM5odzQ6g8GWwXpsnCm1Y2d9rif3eVRaLFqKkjVacSMDOi7StcGVEKV19ijOl5iIMNxqIih9VqO5WUC1DM9oIAB49gw2+llrfcm0Ks3slqcc/lFghg9SyynZdUIJNpj9iNVCWNpdiljZALFZLdgyWG9qdBxKuPP8NBUkY/+mfuzf1I/+GkdtYFFqDG46scnhpX/9YANeu2yK5mXSVpqKRR3GOl+9pnIo/ZREa3EK7l401vR0q7qTCrMQomzudc03zG3AOZNKHLwBCdE2bF08zmXGDn2g48SyNGTJgyeb1YIJJak4vjkXm2cNyVwedJKVxZ1URWHDjGr7Z3jCA+dMwDNrurD7ksn2lIhxkTb7FLcapbrXbac0Y0Z9NuY05WL7eRM1L4rp9TloKkgy1IBumlWLVb1l9hf+oo5iHFOXjVfW9xoGks1SdcbR4VaMzk4wvC7A2NvXUZaGSeXpTiUOekY5iUjXZzO5aKDS8HdJjY3AcXJKNPXljB81NHhy5lU/aWwBDmweMJzBWNRRjGtPqENKrPTi12euUGOUck0MWcxHLcpXd64XNY+rZz/Mjczp9AmFHnySlqhwKxZ3jkJ1jrY9T6sNjVSV0+tzcGKr1I6dZa8xYy/r95lQMjRAiLJZ8exa7eC63I3s6urZtbj0mCq742dWYy7Om1yG5d3mZnqNZF3qdJCvX96HOxe0YvWUcvu6nsrhea7/9/xJOGms877/8umjnW4zS11eIv5w9gSvj1ccHmunVjhsU8tS3RGK3VJgSxgxQdGmDpfKrHg8urwDJemxqFz3CL4//CPuX6ydlvdGtxdmtSDWasEZHcVY2F6Ebbs+sJcz1VOXl+igqRxOuVtAG6hntRCeW9uN7w4dRsuV2w3313uCfr9oLPJTovHs25+jsywNRIQJozzTfV4/2IDH9x60G2VXzdYWZ1BLTtpKUvGXfZ8BAA6beMMoOb5L02Pxj8//DQB4+8p+fPvDYVRf+qjLY+vyXHsYJldl4PG9B/HyJb1IUE2dqQturJlagT+9+iHe++JbzXmjbFZ8+8NhPLpcyiGeHBOOc7uGtHqJ0eH4uQvj49JjR2PbSx+4vD4FI235b+ZLMpC8pCi8/tHXps6jJzM+EtvP69RUNZten4Pp9ZLXeOqWHfb1vaMzcKVO699TmaExgj0s2AYA9oC27soMjB+V6tLzNNVgIKsQii8mf9BblYHjdFIcpXkIIXDfWePw4ZffYtk9u4d28OB3Ue6/UVCWuxLWF/RV4I5nDjhk+lHz+uV9LmMgZjTk4LYd++3LVdnmBoTB5L/PmYALtr7ishCHs1fLYEs+1j2wB4A04FQHLZuRVCTFhOP0CUU4sbUAAgI2qwVLPIifmNuSj6nVWWi/yrhqYVS41T6j2FWRjj+/8YnTrFJG31E/iwnAqVRqyugMPLrnII5vzsMl8j0JFsrg/8yJo7BJV3zEk9nOUMwEwh5mxhTlmXGwWggvruvBy5f0+iQVlRoiwnFNuQ5VBxWD5ziD6a/h6lDVxlRMeBgSom2GuVyd0VqcgqyEKMxqzLUPGFJiIzQaSXekxEaYShU2Ki0Gdy5sxbTaLEwZnWF/AStTnXoeXNJmr1CmNsKtFkJsRJhDgImn3HZKMw5sHtAYy0b84ewJDoOrR5a34+dzG9x6gPTsWjcZ71zZr/HUuxs3mB3HqT3ZN53YiP6aTGTGR9rlIsoLeLlc0Ejv8Y1VyVj0nmsz081KGjxvUrPFR9owrzVfM2h1le7Rfl0hGVbjP249pRlTdCnflIHh/LYijClMdnBoGN2jC/sdPWfAUH+iz9180th8TKvNRkZ8BK46rlaz7XzZ+xhps2Jpdym2OfHs1eUlIirc6tIxMTo7AQc2D2Dl5DLU5yXCZrXg8RUdsJAkK3rtsilOjw00P5/bgC2D9ajPS8SjKzrs6f+m1WZh0yzt4DI7MQozG3Jwy0layZE0Kyc5KKwWAhHh6fM7cd0J9bh6juuqkGrCwyyGWZMUOZTRMxkeZkFcpA15JnJ5A8AvTxuDA5sHUJ4ZZzjoMXqfJpmsdBsTbsUtJzVh/6Z+n+rnvcnTDmgHh1fP1rZ3dzMtoQ57mBmPcKcL8zXLe0rx3aHDOL5ZmupSvJPNBUkIG2Y1Q4uFcMdpY/DIax9jreoluGWwHq+8/xX++6UP8Pk33wNwHlzoDb84tdlUHmpA5QGTl5Vgw3t3voe7X3gPFZlxmNOUi/tefF9znDpww8h40r94t53tu0A+/XXog0gKUmJQkOKZXr+jLE3zPV64sBvdP3sal89wPQXpKnvJvNZ8PLb3IABJK/33g/9CTmIUYiLCHKRFP5/XgH9+8wMSo2247om3sLqvXLP9r2uda63NpEeqyU0Ydro4AHjx4h5E2qz46tsfMH7zn13uq8xq6L2uRxOpsREO9/32U5rx5zc/we+ef9ewjPOijlG48iHHsr1KU9M7k6+YIRmAz1/YAwBYff8r9m1jVFliXFVH9cTMWNpdas+uUJoRh3c2Db9d+RpnM4n6YGpAMoavdRJEdt0JDdj64vsYLXvTvelbnHHS2AKkxkagpSgZjRse125U/cYL24pw+1/2wyxjCpOx4y1ppvDMjmIcW5+NQt01rz+mCp3lxrp6PdvOnqAqAmTcUlb0lOHaJ/7u9lzqWduxXmbJOayuKRA71Gdf0Ffhs/LqwYINZiakSYwOx6ZZQ6PU3ZdOxuI7d2GNgT7KGyZVpGOSLuBHmVp/dM/HwDdSUGNJmnlvqBJweMq4AsNS6t0e6dh0FrPMnKZcjB+VgtykaDQVJOO83nK88v6XWPTbFwEASTFag/yhpe149YMvNesKUqLtUo1GEwUZgsUbG/ocpifT4yPxqgmPmStDo7M8HU+t6sQ7n/0fADhNVQZIQXeZCdLLRG1gZcZH4uOv/+MQ6KpG7VHqLE/H3JY8LDOpk/QURcesDtx05rXOiI/0iZF+pNFTlYGuinSc0JznVJq0Y/UkvPTel1h690v2dYoxYHYwfM4k5znZn1rViY+//g8Gb30OwMgpaR1o0uIi/Frgoq/auAiNegy8pLvUbjCbye+vHvifP6Vc4/h5YuVE/Pv7Q6jNNR90p56pc5atqcRkkPOzTkree8JhVQRzTc7Q90iVjecnVk5EzzVPGx5blhGLvx/8v2Ffg79gg5kZUUSEWfHL07yr/OQp7aVpuPuFd1GUGutWeqBmcEwe0uMi0F2ZPuycrMpUv16+QEQa71dmQiQyEzLxzJouQwOpKjveQdP40NJ27HjrM68rtAUKZwF9ptDdfv13LUyN8bq4AgA8uaoT3x82SHEBSUbTXpqmyaUaHmbRDAD9yRntRbhtx35TmUYYLRYLudTx5yVHIzcpCge/+g8Somz4r/99G4myoXxuVwnS4iJw2f/sMdSmb5pVg5iIMBzrxMsKOLZLtpdDi4lO0lWe11uGGQ05+Pd3h5wee0FfBR7Y/SEARwPXrGHrDKuFsHFmNS76w2ua9WbkVx1laaYHe66vYWgAkBYXgVmNOdi2ayjmpCQ9FmOLk/HcO18AAO4+Yyzm3iYNDL2J5QgkbDAzjBMunz4aiyeOMqUHVWOxEHp8lMczMyES9ywaa+ipNsIT4zcmIsypB+VIQV9m/A4vy+w6Iyrciig4GvRvX9kPQnADVy4aqMJFA1Xud2S8gojsadKOV1WHi7RZcer4QkytzsRX3/7gcJyzTBFGvLK+F8vv2e2zGTVm+Dy2ogP5Ku1yQpQNlx5ThZ7KDMRF2lDvJmA6W9VHe+JQcReIrTA4Jt9uMB9Tl22Xq6h5fEUHEqJsSI2NwNl37cIjez7Grzx0RKlnp9TBz2U6oz9PduykqtJu5iRGA/gCV82uxThVoHxyTDh6KtOx/Y1PPLqWQMEGM8M4wWa1BLyAgxHeaskYyeNyzfF1WHnvywH/XOboJj0+0qMgYiPiI20Bm1Fj3LO0q8RQunX6BP/nvN6mC54eV5ximAnFaiH0VmXgsb0HMVCThb7qTLzzqVbmUJQaY5eCbJlbjy+++d7U4H7LYL02i4zMS+smI8xK2P3elw7vq3O7SlCdk4BOlVd+Xmse7t/1PsbrskrdOK/Rqzz/gYINZoZhjhp8VESQYZijkJW95e53MsHDy9rx17c/9+gY/SD8bhdZLBQ9veLALk6Lxb6NUzH7lmex+70vNeeKCLNqCn2puX/xeBx3819hIdiDR40MZiWjR3upo1TFZrVo0o0CUu0GtYf6quNq0ViQFNLGMsAGM8MwRzjqWc8RHqTNMEwQ2DSrBr97/l2fna8yK9504aSLByrx4Zf/8ej8648djbS4CHSpAtrDrBb8en4LDnz2jWkpSFNBElb3lWOSKmPHxpnVwy6frUctaQplyJvSn4GkublZ7Ny5M9iXwTDMCGXvh1+j//odKE6LwfaVE4cdiMkwDMMcmRDRi0IIw9ro7GFmGOaIpio7HrvWTfY4eJNhGIZhFHiCkmGYIx42lhmGYZjhwAYzwzAMwzAMw7iADWaGYRiGYRiGcQEbzAzDMAzDMAzjAjaYGYZhGIZhGMYFbDAzDMMwDMMwjAvYYGYYhmEYhmEYF7DBzDAMwzAMwzAuYIOZYRiGYRiGYVzABjPDMAzDMAzDuIANZoZhGIZhGIZxAQkhgn0NLiGiTwH8IwgfnQrgsyB8LjPy4LbCmIXbCmMWbiuMWbit+I4CIUSa0YaQN5iDBRHtFEI0B/s6mNCH2wpjFm4rjFm4rTBm4bYSGFiSwTAMwzAMwzAuYIOZYRiGYRiGYVzABrNzbg32BTAjBm4rjFm4rTBm4bbCmIXbSgBgDTPDMAzDMAzDuIA9zAzDMAzDMAzjAjaYDSCiPiJ6k4j2EdGaYF8PE1iIKI+IniSivUS0h4iWyeuTiehxInpL/j9JXk9EdL3cXl4hokbVuU6V93+LiE4N1ndi/AsRWYnoJSJ6UF4uIqLn5TbxeyIKl9dHyMv75O2FqnOslde/SURTgvNNGH9CRIlEtJWI3iCi14loHPcrjBFEtEJ+/7xGRHcTUST3K8GFDWYdRGQFcCOAqQCqAMwloqrgXhUTYA4BOE8IUQVgLIBz5DawBsB2IUQpgO3yMiC1lVL53yIANwOSgQ3gUgCtAFoAXKq8DJkjjmUAXlct/wTAtUKIEgD/BLBAXr8AwD/l9dfK+0FuX4MARgPoA3CT3BcxRxZbADwihKgAUAepzXC/wmggohwASwE0CyGqAVgh9Q/crwQRNpgdaQGwTwjxjhDiewD3AJge5GtiAogQ4iMhxC75739BeqnlQGoHv5Z3+zWAGfLf0wH8Rkg8ByCRiLIATAHwuBDiCyHEPwE8DqnTYo4giCgXwACA2+VlAtAFYKu8i76tKG1oK4Buef/pAO4RQnwnhNgPYB+kvog5QiCiBAAdAH4BAEKI74UQX4L7FcaYMABRRBQGIBrAR+B+JaiwwexIDoD3VMvvy+uYoxB5aqsBwPMAMoQQH8mbPgaQIf/trM1wWzo6uA7AagA/ysspAL4UQhySl9W/u71NyNu/kvfntnLkUwTgUwB3yPKd24koBtyvMDqEEB8A+CmAdyEZyl8BeBHcrwQVNpgZxglEFAvgfgDLhRBfq7cJKb0Mp5g5yiGiaQA+EUK8GOxrYUKeMACNAG4WQjQA+AZD8gsA3K8wErLEZjqkQVY2gBjwLELQYYPZkQ8A5KmWc+V1zFEEEdkgGct3CSG2yasPylOikP//RF7vrM1wWzrymQDgWCI6AEm+1QVJp5ooT6UC2t/d3ibk7QkAPge3laOB9wG8L4R4Xl7eCsmA5n6F0dMDYL8Q4lMhxA8AtkHqa7hfCSJsMDvyNwClcjRqOCTB/B+DfE1MAJG1X78A8LoQ4hrVpj8CUCLSTwXwgGr9KXJU+1gAX8lTrI8C6CWiJNlj0CuvY44QhBBrhRC5QohCSH3Fn4UQJwJ4EsBseTd9W1Ha0Gx5fyGvH5Sj3YsgBXq9EKCvwQQAIcTHAN4jonJ5VTeAveB+hXHkXQBjiShafh8pbYX7lSAS5n6XowshxCEiOhdSB2QF8EshxJ4gXxYTWCYAOBnAq0S0W153IYDNAO4logUA/gHgeHnbQwD6IQVU/BvA6QAghPiCiDZAGoQBwOVCiC8C8xWYIHMBgHuI6AoAL0EO9JL//y0R7QPwBSQjG0KIPUR0L6SX4iEA5wghDgf+shk/swTAXbIz5h1IfYUF3K8wKoQQzxPRVgC7IPUHL0Gq5vcncL8SNLjSH8MwDMMwDMO4gCUZDMMwDMMwDOMCNpgZhmEYhmEYxgVsMDMMwzAMwzCMC9hgZhiGYRiGYRgXsMHMMAzDMAzDMC5gg5lhGIZhGIZhXMAGM8MwDMMwDMO4gA1mhmEYhmEYhnHB/wPETC7BMZxAXwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot(y='AvgRate',figsize=(12,5), title=\"AvgRate of network traffic data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:33.971770Z",
     "start_time": "2020-04-05T09:42:33.827516Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot(y='total',figsize=(12,5), title=\"total bytes of network traffic data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time series forecasting with _AutoTS_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_AutoTS_ provides AutoML support for building end-to-end time series analysis pipelines (including automatic feature generation, model selection and hyperparameter tuning).\n",
    "\n",
    "The general workflow using automated training contains below two steps. \n",
    "   1. create a ```AutoTSTrainer``` to train a ```TSPipeline```, save it to file to use later or elsewhere if you wish.\n",
    "   2. use ```TSPipeline``` to do prediction, evaluation, and incremental fitting as well. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, you need to initialize RayOnSpark before using auto training (i.e. ```AutoTSTrainer```), and stop it after training finished. (Note RayOnSpark is not needed if you just use `TSPipeline` for inference, evaluation or incremental training.)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:51.917822Z",
     "start_time": "2020-04-05T09:42:34.581511Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current pyspark location is : /home/liangs/spark/python/lib/pyspark.zip/pyspark/__init__.py\n",
      "Start to getOrCreate SparkContext\n",
      "pyspark_submit_args is:  --driver-class-path /home/liangs/BigDL/dist/lib/bigdl-dllib-spark_2.4.6-0.14.0-SNAPSHOT-jar-with-dependencies.jar:/home/liangs/BigDL/dist/lib/bigdl-orca-spark_2.4.6-0.14.0-SNAPSHOT-jar-with-dependencies.jar pyspark-shell \n",
      "Successfully got a SparkContext\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-10-29 18:20:30,525\tINFO services.py:1174 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://10.239.44.108:8266\u001b[39m\u001b[22m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'node_ip_address': '10.239.44.108', 'raylet_ip_address': '10.239.44.108', 'redis_address': '10.239.44.108:17878', 'object_store_address': '/tmp/ray/session_2021-10-29_18-20-29_815504_12388/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2021-10-29_18-20-29_815504_12388/sockets/raylet', 'webui_url': '10.239.44.108:8266', 'session_dir': '/tmp/ray/session_2021-10-29_18-20-29_815504_12388', 'metrics_export_port': 57973, 'node_id': '8f643de5118cc5531e36572f8bc5481206ed9bc4c433dd6b04fe1687'}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'node_ip_address': '10.239.44.108',\n",
       " 'raylet_ip_address': '10.239.44.108',\n",
       " 'redis_address': '10.239.44.108:17878',\n",
       " 'object_store_address': '/tmp/ray/session_2021-10-29_18-20-29_815504_12388/sockets/plasma_store',\n",
       " 'raylet_socket_name': '/tmp/ray/session_2021-10-29_18-20-29_815504_12388/sockets/raylet',\n",
       " 'webui_url': '10.239.44.108:8266',\n",
       " 'session_dir': '/tmp/ray/session_2021-10-29_18-20-29_815504_12388',\n",
       " 'metrics_export_port': 57973,\n",
       " 'node_id': '8f643de5118cc5531e36572f8bc5481206ed9bc4c433dd6b04fe1687'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# init RayOnSpark in local mode\n",
    "from bigdl.dllib.nncontext import init_spark_on_local\n",
    "from bigdl.orca.ray import OrcaRayContext\n",
    "sc = init_spark_on_local(cores=4, spark_log_level=\"INFO\")\n",
    "ray_ctx = OrcaRayContext(sc=sc, object_store_memory=\"1g\")\n",
    "ray_ctx.init()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we initialize a `AutoTSTrainer`.\n",
    "* `dt_col`: the column specifying datetime.\n",
    "* `target_col`: target column to predict. Here, we take `AvgRate` KPI as an example.\n",
    "* `horizon` : num of steps to look forward. \n",
    "* `extra_feature_col`: a list of columns which are also included in input data frame as features except target column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:42:53.144776Z",
     "start_time": "2020-04-05T09:42:51.919760Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/liangs/BigDL/python/chronos/src/bigdl/chronos/utils.py:28: DeprecationWarning: SmokeRecipe will be deprecated in future release. Please use `bigdl.orca.automl.hp` instead.\n",
      "  category=DeprecationWarning)\n",
      "/home/liangs/BigDL/python/chronos/src/bigdl/chronos/utils.py:28: DeprecationWarning: SmokeRecipe will be deprecated in future release. Please use `bigdl.orca.automl.hp` instead.\n",
      "  category=DeprecationWarning)\n",
      "/home/liangs/BigDL/python/chronos/src/bigdl/chronos/utils.py:28: DeprecationWarning: AutoTSTrainer will be deprecated in future release. Please use `bigdl.chronos.autots.AutoTSEstimator` instead.\n",
      "  category=DeprecationWarning)\n",
      "/home/liangs/BigDL/python/chronos/src/bigdl/chronos/utils.py:28: DeprecationWarning: TimeSequencePredictor will be deprecated in future release. Please use `bigdl.chronos.autots.AutoTSEstimator` instead.\n",
      "  category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from bigdl.chronos.autots.deprecated.forecast import AutoTSTrainer\n",
    "\n",
    "trainer = AutoTSTrainer(dt_col=\"datetime\",\n",
    "                        target_col=\"AvgRate\",\n",
    "                        horizon=1,\n",
    "                        extra_features_col=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can set some searching presets such as `look_back` which indicates the history time period we want to use for forecasting.\n",
    "lookback can be an int which it is a fixed values, or can be a tuple to indicate the range for sampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:44:10.228166Z",
     "start_time": "2020-04-05T09:44:10.225074Z"
    }
   },
   "outputs": [],
   "source": [
    "# look back in range from one week to 3 days to predict the next 2h.\n",
    "look_back = (36, 84)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to split the data frame into train, validation and test data frame before training. You can use `train_val_test_split` as an easy way to finish it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:44:56.311570Z",
     "start_time": "2020-04-05T09:44:56.298100Z"
    }
   },
   "outputs": [],
   "source": [
    "from bigdl.chronos.autots.deprecated.preprocessing.utils import train_val_test_split\n",
    "train_df, val_df, test_df = train_val_test_split(df, \n",
    "                                                 val_ratio=0.1, \n",
    "                                                 test_ratio=0.1,\n",
    "                                                 look_back=look_back[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we fit on train data and validation data. \n",
    "\n",
    "You can use `recipe` to specify searching method as well as other searching presets such as stop criteria .etc. The `GridRandomRecipe` here is a recipe that combines grid search with random search to find the best set of parameters. For more details, please refer to bigdl document [here](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/autots.html#recipe)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:44:59.067809Z",
     "start_time": "2020-04-05T09:44:59.062375Z"
    }
   },
   "outputs": [],
   "source": [
    "from bigdl.chronos.autots.deprecated.config.recipe import LSTMGridRandomRecipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:48:03.575923Z",
     "start_time": "2020-04-05T09:45:31.368537Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "== Status ==<br>Memory usage on this node: 7.9/125.5 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 0/4 CPUs, 0/0 GPUs, 0.0/110.21 GiB heap, 0.0/0.63 GiB objects<br>Current best trial: daf5f_00001 with mse=0.058848995715379715 and parameters={'model': 'LSTM', 'lstm_1_units': 128, 'dropout_1': 0.2, 'lstm_2_units': 32, 'dropout_2': 0.35380016985073287, 'lr': 0.007096092488609188, 'batch_size': 64, 'epochs': 1, 'past_seq_len': 57}<br>Result logdir: /home/liangs/zoo_automl_logs/automl<br>Number of trials: 3/3 (3 TERMINATED)<br><br>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-10-29 18:27:24,479\tINFO tune.py:450 -- Total run time: 408.89 seconds (408.81 seconds for the tuning loop).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/liangs/anaconda3/envs/env/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/liangs/anaconda3/envs/env/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:521: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n",
      "  tensor_proto.tensor_content = nparray.tostring()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs.\n",
      "CPU times: user 17.4 s, sys: 2.83 s, total: 20.2 s\n",
      "Wall time: 6min 49s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ts_pipeline = trainer.fit(train_df, val_df, \n",
    "                          recipe=LSTMGridRandomRecipe(\n",
    "                              num_rand_samples=1,\n",
    "                              epochs=1,\n",
    "                              look_back=look_back, \n",
    "                              batch_size=[64]),\n",
    "                          metric=\"mse\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a `TSPipeline` after training. Let's print the hyper paramters selected.\n",
    "Note that `past_seq_len` is the lookback value that is automatically chosen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:48:10.610297Z",
     "start_time": "2020-04-05T09:48:10.607153Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean': [470.45753424657534,\n",
       "  0.0,\n",
       "  15.558219178082192,\n",
       "  155.625,\n",
       "  11.0,\n",
       "  2.98972602739726,\n",
       "  22.626712328767123,\n",
       "  5.633561643835616,\n",
       "  0.8333333333333334,\n",
       "  0.25,\n",
       "  0.2842465753424658],\n",
       " 'scale': [237.54721125499952,\n",
       "  1.0,\n",
       "  8.827884346441833,\n",
       "  98.41993890975547,\n",
       "  6.904105059069326,\n",
       "  2.000829603694729,\n",
       "  14.008597121082714,\n",
       "  3.2248983714954504,\n",
       "  0.3726779962499649,\n",
       "  0.4330127018922193,\n",
       "  0.45105483009113834],\n",
       " 'future_seq_len': 1,\n",
       " 'dt_col': 'datetime',\n",
       " 'target_col': ['AvgRate'],\n",
       " 'extra_features_col': None,\n",
       " 'drop_missing': True,\n",
       " 'model': 'LSTM',\n",
       " 'lstm_1_units': 128,\n",
       " 'dropout_1': 0.2,\n",
       " 'lstm_2_units': 32,\n",
       " 'dropout_2': 0.35380016985073287,\n",
       " 'lr': 0.007096092488609188,\n",
       " 'batch_size': 64,\n",
       " 'epochs': 1,\n",
       " 'past_seq_len': 57,\n",
       " 'check_optional_config': False,\n",
       " 'input_dim': 11,\n",
       " 'output_dim': 1,\n",
       " 'input_feature_num': 11,\n",
       " 'output_feature_num': 1}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_pipeline.internal.config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use it to do prediction, evaluation or incremental fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:48:18.212911Z",
     "start_time": "2020-04-05T09:48:17.672724Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_df = ts_pipeline.predict(test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot actual and prediction values for `AvgRate` KPI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:48:20.239860Z",
     "start_time": "2020-04-05T09:48:20.074644Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the predicted values and actual values\n",
    "plot_result(test_df, pred_df, dt_col=\"datetime\", value_col=\"AvgRate\", look_back=ts_pipeline.internal.config['past_seq_len'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate mean square error and the symetric mean absolute percentage error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:48:24.353282Z",
     "start_time": "2020-04-05T09:48:23.926760Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate: the mean square error is 5123.332412044246\n",
      "Evaluate: the smape value is 9.023943508008637\n"
     ]
    }
   ],
   "source": [
    "mse, smape = ts_pipeline.evaluate(test_df, metrics=[\"mse\", \"smape\"])\n",
    "print(\"Evaluate: the mean square error is\", mse)\n",
    "print(\"Evaluate: the smape value is\", smape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can save the pipeline to file and reload it to do incremental fitting or others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:48:30.202808Z",
     "start_time": "2020-04-05T09:48:30.023909Z"
    }
   },
   "outputs": [],
   "source": [
    "# save pipeline file\n",
    "my_ppl_file_path = ts_pipeline.save(\"/tmp/saved_pipeline/my.ppl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can stop RayOnSpark after auto training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:48:33.286478Z",
     "start_time": "2020-04-05T09:48:32.916403Z"
    }
   },
   "outputs": [],
   "source": [
    "# stop\n",
    "ray_ctx.stop()\n",
    "sc.stop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we demonstrate how to do incremental fitting with your saved pipeline file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First load saved pipeline file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:48:37.523732Z",
     "start_time": "2020-04-05T09:48:35.745574Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/liangs/BigDL/python/chronos/src/bigdl/chronos/utils.py:28: DeprecationWarning: load will be deprecated in future release. Please use `bigdl.chronos.autots.TSPipeline` instead.\n",
      "  category=DeprecationWarning)\n",
      "/home/liangs/BigDL/python/chronos/src/bigdl/chronos/utils.py:28: DeprecationWarning: load_ts_pipeline will be deprecated in future release. Please use `bigdl.chronos.autots.TSPipeline` instead.\n",
      "  category=DeprecationWarning)\n",
      "/home/liangs/anaconda3/envs/env/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:521: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n",
      "  tensor_proto.tensor_content = nparray.tostring()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Restore pipeline from /tmp/saved_pipeline/my.ppl\n"
     ]
    }
   ],
   "source": [
    "# load file\n",
    "from bigdl.chronos.autots.deprecated.forecast import TSPipeline\n",
    "loaded_ppl = TSPipeline.load(my_ppl_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then do incremental fitting with `TSPipeline.fit()`.We use validation data frame as additional data for demonstration. You can use your new data frame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:50:10.820564Z",
     "start_time": "2020-04-05T09:50:09.321120Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/liangs/anaconda3/envs/env/lib/python3.6/site-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/liangs/anaconda3/envs/env/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:521: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n",
      "  tensor_proto.tensor_content = nparray.tostring()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 855 samples, validate on 855 samples\n",
      "Epoch 1/2\n",
      "855/855 [==============================] - 3s 4ms/sample - loss: 0.0991 - mean_squared_error: 0.0991 - val_loss: 0.0738 - val_mean_squared_error: 0.0738\n",
      "Epoch 2/2\n",
      "855/855 [==============================] - 3s 3ms/sample - loss: 0.0819 - mean_squared_error: 0.0819 - val_loss: 0.0565 - val_mean_squared_error: 0.0565\n",
      "Fit done!\n"
     ]
    }
   ],
   "source": [
    "# we use validation data frame as additional data for demonstration. \n",
    "loaded_ppl.fit(val_df, epochs=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "predict and plot the result after incremental fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:50:13.535064Z",
     "start_time": "2020-04-05T09:50:13.007472Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# predict results of test_df\n",
    "new_pred_df = loaded_ppl.predict(test_df)\n",
    "plot_result(test_df, new_pred_df, look_back=loaded_ppl.internal.config['past_seq_len'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate mean square error and the symetric mean absolute percentage error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-05T09:50:16.412656Z",
     "start_time": "2020-04-05T09:50:15.986206Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate: the mean square error is 3315.7247410554187\n",
      "Evaluate: the smape value is 7.376407878962388\n"
     ]
    }
   ],
   "source": [
    "# evaluate test_df\n",
    "mse, smape = loaded_ppl.evaluate(test_df, metrics=[\"mse\", \"smape\"])\n",
    "print(\"Evaluate: the mean square error is\", mse)\n",
    "print(\"Evaluate: the smape value is\", smape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "5fd0d186da03a3cc4467b80ade5453b676349bbfc4f1af2284fe352fa0ffaaf6"
  },
  "kernelspec": {
   "display_name": "Python 3.6.10 64-bit ('env': conda)",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
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 },
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